{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzVsNhi1jbAE"
      },
      "source": [
        "# **Spotlight Kampala**\n",
        "# Analysis for manuscript \"Grid connections and inequitable access to electricity in African cities\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGTrDoWpjo13"
      },
      "source": [
        "# Import libraries, data and pre-analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYLw3OmCjxl-"
      },
      "source": [
        "### Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 518,
      "metadata": {
        "id": "axMz6SILj2GQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from cycler import cycler\n",
        "import numpy as np\n",
        "import string\n",
        "import matplotlib.patches as mpatches\n",
        "import matplotlib as mpl\n",
        "from matplotlib import rcParams\n",
        "import seaborn as sns\n",
        "from google.colab import drive\n",
        "import matplotlib.ticker as mtick\n",
        "from matplotlib.ticker import PercentFormatter\n",
        "from IPython.display import display\n",
        "import re\n",
        "from  matplotlib.colors import LinearSegmentedColormap, to_rgba, rgb2hex\n",
        "import scipy.stats as stats\n",
        "import plotly.graph_objects as go"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sB_1rO3rj7AY"
      },
      "source": [
        "### Set global formatting parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 519,
      "metadata": {
        "id": "p1c5GRWWjeR8"
      },
      "outputs": [],
      "source": [
        "# Set global formatting variables\n",
        "\n",
        "rcParams[\"font.sans-serif\"] = [\"DejaVu Sans\"]\n",
        "rcParams[\"font.family\"] = \"sans-serif\"\n",
        "\n",
        "\n",
        "# Set the default text font size\n",
        "plt.rc('font', size=18)\n",
        "# Set the axes title font size\n",
        "plt.rc('axes', titlesize=18)\n",
        "# Set the font size for x tick labels\n",
        "plt.rc('xtick', labelsize=18)\n",
        "# Set the font size for y tick labels\n",
        "plt.rc('ytick', labelsize=18)\n",
        "# Set the legend font size\n",
        "plt.rc('legend', fontsize=18)\n",
        "# Set the font size of the figure title\n",
        "plt.rc('figure', titlesize=20)\n",
        "\n",
        "colors = ['#089099',\n",
        "          '#7CCBA2',\n",
        "          '#FCDE9C',\n",
        "          '#F0746E',\n",
        "          '#DC3977',\n",
        "          '#7C1D6F']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HItUeSTkC8Q"
      },
      "source": [
        "### Define file path and read in survey data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 520,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ET9zF1gkAan",
        "outputId": "cbe88c69-4d09-482b-8d02-5c7550ce1b26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')\n",
        "path = ('/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/Surveys/')\n",
        "fig_path = ('/content/drive/My Drive/Spotlight Kampala/Outputs/Grid connections and inequitable access to electricity/Graphics/')\n",
        "\n",
        "df = pd.read_csv(path + 'Spotlight Kampala Final Survey Dataset.csv')\n",
        "df = df.iloc[:-1] # Drop the last row which has NaNs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gS6UUGKlkx1n"
      },
      "source": [
        "### Uniformize connection types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 521,
      "metadata": {
        "id": "x6iLNQJKkiLt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "eb5288ed-2a03-4154-fc65-a0f0ce30c8f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "connection_type\n",
              "1     24\n",
              "2    122\n",
              "3     14\n",
              "4      6\n",
              "5    225\n",
              "6     86\n",
              "7     21\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>connection_type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define mapping of enumerators to connection type decryption\n",
        "connection_mapping = {\n",
        "    'Bulenza (Enumerator 1) (599747)': {'A': 2, 'B': 3, 'C': 4, 'D': 1, 'E': 3, 'F': 6, 'G': 7, 'H': 5, 'I': 6},\n",
        "    'Sumaya (Enumerator 1) (599747)': {'A': 2, 'B': 3, 'C': 4, 'D': 1, 'E': 3, 'F': 6, 'G': 7, 'H': 5, 'I': 6},\n",
        "    'Harry (Enumerator 2) (955451)': {'A': 1, 'B': 6, 'C': 5, 'D': 2, 'E': 3, 'F': 6, 'G': 3, 'H': 7, 'I': 4},\n",
        "    'Fatumah (Enumerator 3) (925225)': {'A': 7, 'B': 1, 'C': 3, 'D': 5, 'E': 6, 'F': 3, 'G': 4, 'H': 2, 'I': 6},\n",
        "    'Molly (Enumerator 4) (683543)': {'A': 6, 'B': 7, 'C': 4, 'D': 3, 'E': 1, 'F': 5, 'G': 2, 'H': 3, 'I': 6},\n",
        "    'Gerald (Enumerator 5) (135231)': {'A': 5, 'B': 6, 'C': 1, 'D': 6, 'E': 2, 'F': 4, 'G': 3, 'H': 7, 'I': 3}\n",
        "}\n",
        "\n",
        "# Apply the mapping vectorized\n",
        "df['connection_type'] = df.apply(\n",
        "    lambda row: connection_mapping.get(row['enumerator'], {}).get(row['connection_type'], np.nan), axis=1\n",
        ")\n",
        "\n",
        "# Compute count of each connection type (excluding NaNs)\n",
        "connection_type_counts = df['connection_type'].value_counts().reindex(range(1, 8), fill_value=0)\n",
        "\n",
        "# Define connection type labels\n",
        "connection_type_labels = [\n",
        "    'Unelectrified',  # 1\n",
        "    'Individual metered',  # 2\n",
        "    'Individual unmetered',  # 3\n",
        "    'Individual dual',  # 4\n",
        "    'Collective metered',  # 5\n",
        "    'Collective unmetered',  # 6\n",
        "    'Collective dual'  # 7\n",
        "]\n",
        "\n",
        "# Print the computed connection type counts\n",
        "display(connection_type_counts)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Uniformize payment recipient categories"
      ],
      "metadata": {
        "id": "rDmQeBtgDwJ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Uniformize/clean payment recipients\n",
        "payment_options = ['umeme',\n",
        "                  'landlord',\n",
        "                  'neighbor',\n",
        "                  'kamyufu',\n",
        "                  'other',\n",
        "                  'umeme no_one',\n",
        "                  'landlord no_one',\n",
        "                  'umeme kamyufu',\n",
        "                  'landlord kamyufu',\n",
        "                  'kamyufu landlord',\n",
        "                  'no_one',\n",
        "                  'landlord neighbor',\n",
        "                  'kamyufu neighbor',\n",
        "                  'umeme landlord'] # All unique responses from survey\n",
        "\n",
        "df['payment_recipient'] = df['payment_to']\n",
        "\n",
        "# Define a function to map the values to the desired categories\n",
        "def map_to_categories(value):\n",
        "    if pd.isna(value) or isinstance(value, float):  # Check for NaN or float values\n",
        "        return np.nan  # Return NaN for missing values\n",
        "    elif value == 'umeme':\n",
        "        return 'Umeme'\n",
        "    elif value == 'landlord':\n",
        "        return 'Landlord'\n",
        "    elif value == 'kamyufu':\n",
        "        return 'Kamyufu'\n",
        "    elif value == 'neighbor':\n",
        "        return 'Neighbor'\n",
        "    elif 'no_one' in value or 'other' in value:\n",
        "        return 'No one/other'\n",
        "    else:\n",
        "        return 'Two bill collectors'\n",
        "\n",
        "# Apply the mapping function to create the new 'payment_category' column\n",
        "df['payment recipient'] = df['payment_recipient'].apply(map_to_categories)\n",
        "\n",
        "# Count the occurrences of each category\n",
        "payment_counts = df['payment recipient'].value_counts(dropna=False)\n",
        "\n",
        "# Print the counts\n",
        "display(payment_counts)"
      ],
      "metadata": {
        "id": "oWq--eHrDwYm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "c3a86873-5876-4fcc-9e5d-3403df55d2de"
      },
      "execution_count": 522,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "payment recipient\n",
              "Umeme                  229\n",
              "Landlord               125\n",
              "Kamyufu                 73\n",
              "Neighbor                31\n",
              "NaN                     26\n",
              "Two bill collectors     11\n",
              "No one/other             5\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>payment recipient</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Umeme</th>\n",
              "      <td>229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Landlord</th>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kamyufu</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neighbor</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Two bill collectors</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No one/other</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Designate service arrangements"
      ],
      "metadata": {
        "id": "iW-ySgkynvLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Define a function to categorize 'sa' based on 'payment recipient' and 'connection_type'\n",
        "def categorize_sa(row):\n",
        "    if row['connection_type'] == 2 and row['payment recipient'] == 'Umeme':\n",
        "        return 'Individual metered - Utility'\n",
        "    elif row['connection_type'] == 5 and row['payment recipient'] == 'Umeme':\n",
        "        return 'Collective metered - Utility'\n",
        "    elif row['connection_type'] == 5 and row['payment recipient'] == 'Landlord':\n",
        "        return 'Collective metered - Landlord'\n",
        "    elif row['connection_type'] == 6 and row['payment recipient'] == 'Kamyufu':\n",
        "        return 'Collective unmetered - Local electrician'\n",
        "    elif row['connection_type'] == 1:\n",
        "        return 'Unelectrified'\n",
        "    else:\n",
        "        return 'Other'\n",
        "\n",
        "# Apply the function to create the new 'sa' column\n",
        "df['sa'] = df.apply(categorize_sa, axis=1)\n",
        "\n",
        "# Print the unique counts of 'sa'\n",
        "display(df['sa'].value_counts(dropna=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "Fp4Hs-e0nz2G",
        "outputId": "2a0ef587-ba36-49dd-a5bb-bb080033cb3f"
      },
      "execution_count": 523,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sa\n",
              "Individual metered - Utility                122\n",
              "Collective metered - Landlord               101\n",
              "Collective metered - Utility                101\n",
              "Other                                        97\n",
              "Collective unmetered - Local electrician     55\n",
              "Unelectrified                                24\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sa</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Individual metered - Utility</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective metered - Landlord</th>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective metered - Utility</th>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective unmetered - Local electrician</th>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unelectrified</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mmpHIGHlGjC"
      },
      "source": [
        "### Assign exchange rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 524,
      "metadata": {
        "id": "38BymvSglIGa"
      },
      "outputs": [],
      "source": [
        "# Assign exchange rate\n",
        "exchange_rate = 3754.95 # Average exchange rate in November 2022 when the survey was completed https://www.investing.com/currencies/usd-ugx-historical-data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5A2Ey_slJMU"
      },
      "source": [
        "### Calculate income quartiles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 525,
      "metadata": {
        "id": "luxZ2oEulIwL"
      },
      "outputs": [],
      "source": [
        "income = df.loc[df['total_income'] > 0, 'total_income']\n",
        "income_np = income.to_numpy()\n",
        "percentiles = [0, 25, 50, 75, 100]\n",
        "percentile_values = np.percentile(income_np, percentiles)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7F9PZYDrwViH"
      },
      "source": [
        "### Calculate electricity burden"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 526,
      "metadata": {
        "id": "Q0mD2M1iwVQf"
      },
      "outputs": [],
      "source": [
        "df[['elec_payment_amount', 'total_income']] = df[['elec_payment_amount', 'total_income']].replace(999, np.nan)\n",
        "df['Electricity_burden'] = df['elec_payment_amount'] / df['total_income']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hbILBMGlR5R"
      },
      "source": [
        "# Figure 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 527,
      "metadata": {
        "id": "7sAiObVClYvr"
      },
      "outputs": [],
      "source": [
        "# Group by connection_type and payment_to, then count occurrences\n",
        "sankey_matrix = df.groupby(['connection_type', 'payment recipient']).size().unstack(fill_value=0)\n",
        "\n",
        "# Manuscript version visualized in DisplayR (https://www.displayr.com/)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate percentage breakdown for 'connection_type' excluding \"Unelectrified\"\n",
        "connection_type_counts = df['connection_type'].value_counts(normalize=True) * 100\n",
        "connection_type_counts = connection_type_counts.rename_axis('Connection type').reset_index(name='Percentage')\n",
        "\n",
        "# Convert numeric connection types to their human-readable labels\n",
        "numeric_connection_labels = connection_type_counts['Connection type'].dropna().astype(int).tolist()\n",
        "connection_type_counts['Connection type'] = [connection_type_labels[i - 1] for i in numeric_connection_labels]\n",
        "\n",
        "# Calculate percentage breakdown for 'payment recipient'\n",
        "payment_recipient_counts = df['payment recipient'].value_counts(normalize=True, dropna=False) * 100\n",
        "payment_recipient_counts = payment_recipient_counts.rename_axis('Payment recipient').reset_index(name='Percentage')\n",
        "\n",
        "# Get total count of respondents\n",
        "total_count = len(df)\n",
        "\n",
        "# Print results for easy copy-pasting\n",
        "print(\"Connection type breakdown (excluding 'Unelectrified') (%):\")\n",
        "print(connection_type_counts.to_string(index=False))\n",
        "\n",
        "print(\"\\nPayment recipient breakdown (%):\")\n",
        "print(payment_recipient_counts.to_string(index=False))\n",
        "\n",
        "# Print total count only once\n",
        "print(f\"\\n(n = {total_count})\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6bhGKcBY_dG",
        "outputId": "d534bf19-36f0-4b12-d065-a0e6588273c6"
      },
      "execution_count": 528,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Connection type breakdown (excluding 'Unelectrified') (%):\n",
            "     Connection type  Percentage\n",
            "  Collective metered   45.180723\n",
            "  Individual metered   24.497992\n",
            "Collective unmetered   17.269076\n",
            "       Unelectrified    4.819277\n",
            "     Collective dual    4.216867\n",
            "Individual unmetered    2.811245\n",
            "     Individual dual    1.204819\n",
            "\n",
            "Payment recipient breakdown (%):\n",
            "  Payment recipient  Percentage\n",
            "              Umeme        45.8\n",
            "           Landlord        25.0\n",
            "            Kamyufu        14.6\n",
            "           Neighbor         6.2\n",
            "                NaN         5.2\n",
            "Two bill collectors         2.2\n",
            "       No one/other         1.0\n",
            "\n",
            "(n = 500)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by connection_type and payment_to, then count occurrences\n",
        "sankey_matrix = df.groupby(['connection_type', 'payment recipient']).size().unstack(fill_value=0)\n",
        "\n",
        "# Extract numeric connection types from the index (some may be NaN, so filter safely)\n",
        "numeric_connection_labels = sankey_matrix.index.tolist()\n",
        "numeric_connection_labels = [int(i) for i in numeric_connection_labels if not pd.isna(i)]\n",
        "\n",
        "# Replace numeric connection types with their human-readable labels\n",
        "sankey_matrix.index = [connection_type_labels[i - 1] for i in numeric_connection_labels]\n",
        "\n",
        "# Rename the index so it becomes a column when resetting\n",
        "sankey_matrix.index.name = 'Connection Type'\n",
        "\n",
        "# Convert to long format for easy copy-pasting\n",
        "sankey_long = sankey_matrix.reset_index().melt(id_vars='Connection Type', var_name='Payment Recipient', value_name='Count')\n",
        "\n",
        "# Display the long-form table\n",
        "display(sankey_long)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1Q03UecjWAlo",
        "outputId": "6c619121-3761-47ab-b46e-38f1e7c577f5"
      },
      "execution_count": 529,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         Connection Type    Payment Recipient  Count\n",
              "0          Unelectrified              Kamyufu      0\n",
              "1     Individual metered              Kamyufu      0\n",
              "2   Individual unmetered              Kamyufu      7\n",
              "3        Individual dual              Kamyufu      0\n",
              "4     Collective metered              Kamyufu      2\n",
              "5   Collective unmetered              Kamyufu     55\n",
              "6        Collective dual              Kamyufu      9\n",
              "7          Unelectrified             Landlord      0\n",
              "8     Individual metered             Landlord      0\n",
              "9   Individual unmetered             Landlord      4\n",
              "10       Individual dual             Landlord      3\n",
              "11    Collective metered             Landlord    101\n",
              "12  Collective unmetered             Landlord     14\n",
              "13       Collective dual             Landlord      2\n",
              "14         Unelectrified             Neighbor      0\n",
              "15    Individual metered             Neighbor      0\n",
              "16  Individual unmetered             Neighbor      0\n",
              "17       Individual dual             Neighbor      0\n",
              "18    Collective metered             Neighbor     18\n",
              "19  Collective unmetered             Neighbor      9\n",
              "20       Collective dual             Neighbor      4\n",
              "21         Unelectrified         No one/other      0\n",
              "22    Individual metered         No one/other      0\n",
              "23  Individual unmetered         No one/other      2\n",
              "24       Individual dual         No one/other      1\n",
              "25    Collective metered         No one/other      0\n",
              "26  Collective unmetered         No one/other      1\n",
              "27       Collective dual         No one/other      1\n",
              "28         Unelectrified  Two bill collectors      0\n",
              "29    Individual metered  Two bill collectors      0\n",
              "30  Individual unmetered  Two bill collectors      0\n",
              "31       Individual dual  Two bill collectors      0\n",
              "32    Collective metered  Two bill collectors      3\n",
              "33  Collective unmetered  Two bill collectors      3\n",
              "34       Collective dual  Two bill collectors      5\n",
              "35         Unelectrified                Umeme      1\n",
              "36    Individual metered                Umeme    122\n",
              "37  Individual unmetered                Umeme      0\n",
              "38       Individual dual                Umeme      2\n",
              "39    Collective metered                Umeme    101\n",
              "40  Collective unmetered                Umeme      3\n",
              "41       Collective dual                Umeme      0"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Connection Type</th>\n",
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              "      <th>2</th>\n",
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              "      <th>9</th>\n",
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              "      <td>Individual dual</td>\n",
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              "      <th>11</th>\n",
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              "      <td>Collective unmetered</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Individual metered</td>\n",
              "      <td>Neighbor</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Individual unmetered</td>\n",
              "      <td>Neighbor</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Individual dual</td>\n",
              "      <td>Neighbor</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>Neighbor</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>Neighbor</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Collective dual</td>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Unelectrified</td>\n",
              "      <td>No one/other</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Individual metered</td>\n",
              "      <td>No one/other</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Individual unmetered</td>\n",
              "      <td>No one/other</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Individual dual</td>\n",
              "      <td>No one/other</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>No one/other</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>No one/other</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>No one/other</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>28</th>\n",
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              "      <td>Two bill collectors</td>\n",
              "      <td>0</td>\n",
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              "      <th>29</th>\n",
              "      <td>Individual metered</td>\n",
              "      <td>Two bill collectors</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Individual unmetered</td>\n",
              "      <td>Two bill collectors</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Individual dual</td>\n",
              "      <td>Two bill collectors</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>Two bill collectors</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>Two bill collectors</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>Two bill collectors</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Unelectrified</td>\n",
              "      <td>Umeme</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Individual metered</td>\n",
              "      <td>Umeme</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Individual unmetered</td>\n",
              "      <td>Umeme</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Individual dual</td>\n",
              "      <td>Umeme</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>Umeme</td>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>Umeme</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>Umeme</td>\n",
              "      <td>0</td>\n",
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              "summary": "{\n  \"name\": \"sankey_long\",\n  \"rows\": 42,\n  \"fields\": [\n    {\n      \"column\": \"Connection Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Unelectrified\",\n          \"Individual metered\",\n          \"Collective unmetered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment Recipient\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Kamyufu\",\n          \"Landlord\",\n          \"Umeme\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          5,\n          18,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Table 1"
      ],
      "metadata": {
        "id": "GtyELKYnsiXQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the relevant service arrangements excluding Unelectrified\n",
        "sa_categories = [\n",
        "    \"Individual metered - Utility\",\n",
        "    \"Collective metered - Utility\",\n",
        "    \"Collective metered - Landlord\",\n",
        "    \"Collective unmetered - Local electrician\"\n",
        "]\n",
        "\n",
        "# Create subsets for the selected service arrangements and the \"Other\" category (excluding Unelectrified)\n",
        "df_subset = df[df[\"sa\"].isin(sa_categories)].copy()\n",
        "df_other = df[(~df[\"sa\"].isin(sa_categories)) & (df[\"sa\"] != \"Unelectrified\")].copy()\n",
        "\n",
        "# Ensure the 'number_Yaka_connections' column is numeric and replace 999 with NaN\n",
        "df_subset[\"number_Yaka_connections\"] = pd.to_numeric(df_subset[\"number_Yaka_connections\"], errors=\"coerce\").replace(999, np.nan)\n",
        "df_other[\"number_Yaka_connections\"] = pd.to_numeric(df_other[\"number_Yaka_connections\"], errors=\"coerce\").replace(999, np.nan)\n",
        "\n",
        "# Group by and calculate initial statistics for selected service arrangements, excluding NaN values\n",
        "grouped_subset = df_subset.groupby(\"sa\").agg(\n",
        "    Average_Users_per_Connection=(\"number_Yaka_connections\", lambda x: (x.dropna().mean() + 1) if not x.dropna().empty else np.nan),\n",
        "    Count=(\"sa\", \"size\")\n",
        ").reset_index()\n",
        "\n",
        "# Calculate initial statistics for the \"Other\" category (excluding Unelectrified)\n",
        "results_other = pd.DataFrame({\n",
        "    \"sa\": [\"Other\"],\n",
        "    \"Average_Users_per_Connection\": [(df_other[\"number_Yaka_connections\"].dropna().mean() + 1) if not df_other.empty else np.nan],\n",
        "    \"Count\": [len(df_other)]\n",
        "})\n",
        "\n",
        "# Calculate payment structure percentages for selected service arrangements\n",
        "payment_structures_subset = df_subset.groupby(\"sa\")[\"payment_structure\"].value_counts(normalize=True).unstack(fill_value=0) * 100\n",
        "\n",
        "# Calculate payment structure percentages for \"Other\" service arrangements\n",
        "if not df_other.empty:\n",
        "    payment_structures_other = df_other[\"payment_structure\"].value_counts(normalize=True).to_frame().T * 100\n",
        "else:\n",
        "    payment_structures_other = pd.DataFrame(columns=[\"flat_rate\", \"both_flat_rate_consumption\", \"metered_consumption\", \"other\"], index=[\"Other\"])\n",
        "\n",
        "# Map payment structure names\n",
        "payment_structure_mapping = {\n",
        "    \"flat_rate\": \"Flat rate\",\n",
        "    \"both_flat_rate_consumption\": \"Hybrid\",\n",
        "    \"metered_consumption\": \"Metered\",\n",
        "    \"other\": \"Other\"\n",
        "}\n",
        "\n",
        "# Apply mapping\n",
        "payment_structures_subset.columns = payment_structures_subset.columns.map(payment_structure_mapping)\n",
        "payment_structures_other.columns = payment_structures_other.columns.map(payment_structure_mapping)\n",
        "\n",
        "# Ensure all expected columns exist\n",
        "expected_columns = [\"Flat rate\", \"Hybrid\", \"Metered\", \"Other\"]\n",
        "for col in expected_columns:\n",
        "    if col not in payment_structures_subset.columns:\n",
        "        payment_structures_subset[col] = 0.0\n",
        "    if col not in payment_structures_other.columns:\n",
        "        payment_structures_other[col] = 0.0\n",
        "\n",
        "payment_structures_subset = payment_structures_subset[expected_columns]\n",
        "payment_structures_other = payment_structures_other[expected_columns]\n",
        "\n",
        "# Normalize percentages excluding 'Other'\n",
        "def normalize_percentages(row):\n",
        "    total_excluding_other = 100 - row.get(\"Other\", 0)\n",
        "    for col in [\"Flat rate\", \"Hybrid\", \"Metered\"]:\n",
        "        if total_excluding_other > 0:\n",
        "            row[col] = row[col] / total_excluding_other * 100\n",
        "    return row\n",
        "\n",
        "payment_structures_subset = payment_structures_subset.apply(normalize_percentages, axis=1)\n",
        "payment_structures_other = payment_structures_other.apply(normalize_percentages, axis=1)\n",
        "\n",
        "payment_structures_subset.drop(columns=\"Other\", inplace=True)\n",
        "payment_structures_other.drop(columns=\"Other\", inplace=True)\n",
        "\n",
        "# Reset index\n",
        "payment_structures_subset.reset_index(inplace=True)\n",
        "payment_structures_other.reset_index(drop=True, inplace=True)\n",
        "payment_structures_other[\"sa\"] = \"Other\"\n",
        "\n",
        "# Merge results for selected service arrangements\n",
        "final_subset_results = pd.merge(grouped_subset, payment_structures_subset, left_on=\"sa\", right_on=\"sa\", how=\"left\")\n",
        "\n",
        "# Merge results for \"Other\" category\n",
        "final_other_results = pd.merge(results_other, payment_structures_other, left_on=\"sa\", right_on=\"sa\", how=\"left\")\n",
        "\n",
        "# Concatenate the final results\n",
        "final_subset_results = pd.concat([final_subset_results, final_other_results], ignore_index=True)\n",
        "\n",
        "# Compute \"Sum or average across sample\" row (excluding Unelectrified)\n",
        "total_n = final_subset_results[\"Count\"].sum()\n",
        "avg_users_per_conn = (final_subset_results[\"Average_Users_per_Connection\"].dropna().mean())  # Drop NaN values before averaging\n",
        "weighted_metered = (final_subset_results[\"Metered\"] * final_subset_results[\"Count\"]).sum() / total_n\n",
        "weighted_flat_rate = (final_subset_results[\"Flat rate\"] * final_subset_results[\"Count\"]).sum() / total_n\n",
        "weighted_hybrid = (final_subset_results[\"Hybrid\"] * final_subset_results[\"Count\"]).sum() / total_n\n",
        "\n",
        "sum_or_avg_row = pd.DataFrame({\n",
        "    \"Service arrangement\": [\"Sum or average across sample\"],\n",
        "    \"n\": [total_n],\n",
        "    \"Average users per connection\": [avg_users_per_conn],\n",
        "    \"Metered\": [weighted_metered],\n",
        "    \"Flat rate\": [weighted_flat_rate],\n",
        "    \"Hybrid\": [weighted_hybrid]\n",
        "})\n",
        "\n",
        "# Rename columns\n",
        "final_subset_results = final_subset_results.rename(columns={\n",
        "    \"sa\": \"Service arrangement\",\n",
        "    \"Count\": \"n\",\n",
        "    \"Average_Users_per_Connection\": \"Average users per connection\"\n",
        "})\n",
        "\n",
        "# Select relevant columns\n",
        "final_subset_results = final_subset_results[[\n",
        "    \"Service arrangement\", \"n\", \"Average users per connection\", \"Metered\", \"Flat rate\", \"Hybrid\"\n",
        "]]\n",
        "\n",
        "# Round all numerical values to three significant figures\n",
        "final_subset_results = final_subset_results.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n",
        "sum_or_avg_row = sum_or_avg_row.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n",
        "\n",
        "# Reorder rows according to user-defined order and include 'Other' and summary row at the end\n",
        "row_order = [3, 1, 0, 2]  # Custom order for known categories\n",
        "if \"Other\" in final_subset_results[\"Service arrangement\"].values:\n",
        "    row_order.append(final_subset_results[final_subset_results[\"Service arrangement\"] == \"Other\"].index[0])  # Ensure 'Other' is last\n",
        "final_subset_results = final_subset_results.iloc[row_order]\n",
        "\n",
        "# Append \"Sum or average across sample\" row\n",
        "final_subset_results = pd.concat([final_subset_results, sum_or_avg_row], ignore_index=True)\n",
        "\n",
        "# Display the final table\n",
        "display(final_subset_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "9VWux_pxsjvU",
        "outputId": "ac336af3-8206-4d4d-9f65-d2e220b9ed77"
      },
      "execution_count": 530,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-530-9dfca5a7639a>:119: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
            "  final_subset_results = final_subset_results.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n",
            "<ipython-input-530-9dfca5a7639a>:120: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
            "  sum_or_avg_row = sum_or_avg_row.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                        Service arrangement    n  \\\n",
              "0              Individual metered - Utility  122   \n",
              "1              Collective metered - Utility  101   \n",
              "2             Collective metered - Landlord  101   \n",
              "3  Collective unmetered - Local electrician   55   \n",
              "4                                     Other   97   \n",
              "5              Sum or average across sample  476   \n",
              "\n",
              "   Average users per connection  Metered  Flat rate  Hybrid  \n",
              "0                         1.025   62.295     13.115  24.590  \n",
              "1                         4.690   48.515     14.851  36.634  \n",
              "2                         6.500    8.911     73.267  17.822  \n",
              "3                        13.800    0.000     81.481  18.519  \n",
              "4                         6.893    8.889     66.667  24.444  \n",
              "5                         6.581   29.963     45.059  24.978  "
            ],
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              "      <th>Service arrangement</th>\n",
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              "      <th>1</th>\n",
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              "      <td>Sum or average across sample</td>\n",
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              "variable_name": "final_subset_results",
              "summary": "{\n  \"name\": \"final_subset_results\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Service arrangement\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Individual metered - Utility\",\n          \"Collective metered - Utility\",\n          \"Sum or average across sample\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157,\n        \"min\": 55,\n        \"max\": 476,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          101,\n          476,\n          55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average users per connection\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.16324108117702,\n        \"min\": 1.025,\n        \"max\": 13.8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.025,\n          4.69,\n          6.581\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Metered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.898491813093155,\n        \"min\": 0.0,\n        \"max\": 62.295,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          62.295,\n          48.515,\n          29.963\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Flat rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.75033505469588,\n        \"min\": 13.115,\n        \"max\": 81.481,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          13.115,\n          14.851,\n          45.059\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hybrid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.7504324725654925,\n        \"min\": 17.822,\n        \"max\": 36.634,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          24.59,\n          36.634,\n          24.978\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Table 2"
      ],
      "metadata": {
        "id": "sWJ0GsUGsmF5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the relevant service arrangements including \"Other\" and \"Unelectrified\"\n",
        "sa_categories = [\n",
        "    \"Individual metered - Utility\",\n",
        "    \"Collective metered - Utility\",\n",
        "    \"Collective metered - Landlord\",\n",
        "    \"Collective unmetered - Local electrician\"\n",
        "]\n",
        "\n",
        "# Ensure numeric conversion and exclude 999 values\n",
        "df[\"total_income\"] = pd.to_numeric(df[\"total_income\"], errors=\"coerce\").replace(999, np.nan)\n",
        "df[\"elec_payment_amount\"] = pd.to_numeric(df[\"elec_payment_amount\"], errors=\"coerce\").replace(999, np.nan)\n",
        "df[\"years_in_community\"] = pd.to_numeric(df[\"years_in_community\"], errors=\"coerce\").replace(999, np.nan)\n",
        "\n",
        "# Convert total income to correct units\n",
        "df[\"adjusted_income\"] = df[\"total_income\"] / exchange_rate * 1000\n",
        "df[\"adjusted_elec_payment_amount\"] = df[\"elec_payment_amount\"] / exchange_rate * 1000\n",
        "\n",
        "# Create subsets for the selected service arrangements, \"Other\" (excluding Unelectrified), and \"Unelectrified\"\n",
        "df_subset = df[df[\"sa\"].isin(sa_categories)].copy()\n",
        "df_other = df[(~df[\"sa\"].isin(sa_categories)) & (df[\"sa\"] != \"Unelectrified\")].copy()\n",
        "df_unelectrified = df[df[\"sa\"] == \"Unelectrified\"].copy()\n",
        "\n",
        "# Group by and calculate statistics for selected service arrangements\n",
        "grouped_subset = df_subset.groupby(\"sa\").agg(\n",
        "    n=(\"sa\", \"size\"),\n",
        "    Tenure_in_Community=(\"years_in_community\", \"mean\"),\n",
        "    Renters=(\"rent_or_own\", lambda x: (x == \"rent\").mean() * 100),\n",
        "    Average_Monthly_Income=(\"adjusted_income\", \"mean\"),\n",
        "    Average_Monthly_Electricity_Bill=(\"adjusted_elec_payment_amount\", \"mean\")\n",
        ").reset_index()\n",
        "\n",
        "# Calculate statistics for \"Other\" service arrangements\n",
        "results_other = pd.DataFrame({\n",
        "    \"sa\": [\"Other\"],\n",
        "    \"n\": [len(df_other)],\n",
        "    \"Tenure_in_Community\": [df_other[\"years_in_community\"].mean()],\n",
        "    \"Renters\": [(df_other[\"rent_or_own\"] == \"rent\").mean() * 100],\n",
        "    \"Average_Monthly_Income\": [df_other[\"adjusted_income\"].mean()],\n",
        "    \"Average_Monthly_Electricity_Bill\": [df_other[\"adjusted_elec_payment_amount\"].mean()]\n",
        "})\n",
        "\n",
        "# Calculate statistics for \"Unelectrified\"\n",
        "results_unelectrified = pd.DataFrame({\n",
        "    \"sa\": [\"Unelectrified\"],\n",
        "    \"n\": [len(df_unelectrified)],\n",
        "    \"Tenure_in_Community\": [df_unelectrified[\"years_in_community\"].mean()],\n",
        "    \"Renters\": [(df_unelectrified[\"rent_or_own\"] == \"rent\").mean() * 100],\n",
        "    \"Average_Monthly_Income\": [df_unelectrified[\"adjusted_income\"].mean()],\n",
        "    \"Average_Monthly_Electricity_Bill\": [0]  # Assume no electricity bill for unelectrified users\n",
        "})\n",
        "\n",
        "# Concatenate results into one DataFrame\n",
        "final_results = pd.concat([grouped_subset, results_other, results_unelectrified], ignore_index=True)\n",
        "\n",
        "# Calculate electricity burden (percentage of income spent on electricity)\n",
        "final_results[\"Electricity Burden\"] = (final_results[\"Average_Monthly_Electricity_Bill\"] / final_results[\"Average_Monthly_Income\"]) * 100\n",
        "\n",
        "# Compute \"Sum or average across sample\" row\n",
        "sum_or_avg_row = pd.DataFrame({\n",
        "    \"Service arrangement\": [\"Sum or average across sample\"],\n",
        "    \"n\": [final_results[\"n\"].sum()],\n",
        "    \"Tenure in community\": [df[\"years_in_community\"].mean()],\n",
        "    \"Renters\": [(df[\"rent_or_own\"] == \"rent\").mean() * 100],\n",
        "    \"Average monthly income\": [df[\"adjusted_income\"].mean()],\n",
        "    \"Average monthly electricity bill\": [df[\"adjusted_elec_payment_amount\"].mean()],\n",
        "    \"Electricity burden\": [(df[\"adjusted_elec_payment_amount\"] / df[\"adjusted_income\"]).mean() * 100]\n",
        "})\n",
        "\n",
        "# Rename columns\n",
        "final_results = final_results.rename(columns={\n",
        "    \"sa\": \"Service arrangement\",\n",
        "    \"Tenure_in_Community\": \"Tenure in community\",\n",
        "    \"Average_Monthly_Income\": \"Average monthly income\",\n",
        "    \"Average_Monthly_Electricity_Bill\": \"Average monthly electricity bill\",\n",
        "    \"Electricity Burden\": \"Electricity burden\"\n",
        "})\n",
        "\n",
        "# Select only relevant columns\n",
        "final_results = final_results[[\n",
        "    \"Service arrangement\", \"n\", \"Tenure in community\", \"Renters\",\n",
        "    \"Average monthly income\", \"Average monthly electricity bill\", \"Electricity burden\"\n",
        "]]\n",
        "\n",
        "# Round all numerical values to three significant figures\n",
        "final_results = final_results.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n",
        "sum_or_avg_row = sum_or_avg_row.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n",
        "\n",
        "# Append \"Sum or average across sample\" row\n",
        "final_results = pd.concat([final_results, sum_or_avg_row], ignore_index=True)\n",
        "final_results = final_results.iloc[[3, 1, 0, 2, 4, 5, 6]].reset_index(drop=True)\n",
        "\n",
        "# Display the final table\n",
        "display(final_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "O2hvpr-3t_d0",
        "outputId": "cbf291f3-6c27-4b2a-d90d-fb0a7593903b"
      },
      "execution_count": 531,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-531-56fc47f689c7>:85: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
            "  final_results = final_results.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n",
            "<ipython-input-531-56fc47f689c7>:86: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
            "  sum_or_avg_row = sum_or_avg_row.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                        Service arrangement    n  Tenure in community  \\\n",
              "0              Individual metered - Utility  122               11.557   \n",
              "1              Collective metered - Utility  101                9.208   \n",
              "2             Collective metered - Landlord  101                5.554   \n",
              "3  Collective unmetered - Local electrician   55                7.182   \n",
              "4                                     Other   97                6.443   \n",
              "5                             Unelectrified   24                7.042   \n",
              "6              Sum or average across sample  500                8.180   \n",
              "\n",
              "   Renters  Average monthly income  Average monthly electricity bill  \\\n",
              "0   50.000                 252.701                             8.352   \n",
              "1   64.356                 199.742                            10.587   \n",
              "2   99.010                 164.378                             5.181   \n",
              "3   87.273                 123.047                             6.304   \n",
              "4   88.660                 140.650                             5.961   \n",
              "5   79.167                 141.994                             0.000   \n",
              "6   75.800                 182.551                             7.440   \n",
              "\n",
              "   Electricity burden  \n",
              "0               3.305  \n",
              "1               5.300  \n",
              "2               3.152  \n",
              "3               5.124  \n",
              "4               4.238  \n",
              "5               0.000  \n",
              "6               5.472  "
            ],
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              "\n",
              "  <div id=\"df-304d320c-8ec4-4eb8-ab8e-79938bd28b0c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Service arrangement</th>\n",
              "      <th>n</th>\n",
              "      <th>Tenure in community</th>\n",
              "      <th>Renters</th>\n",
              "      <th>Average monthly income</th>\n",
              "      <th>Average monthly electricity bill</th>\n",
              "      <th>Electricity burden</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Individual metered - Utility</td>\n",
              "      <td>122</td>\n",
              "      <td>11.557</td>\n",
              "      <td>50.000</td>\n",
              "      <td>252.701</td>\n",
              "      <td>8.352</td>\n",
              "      <td>3.305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Collective metered - Utility</td>\n",
              "      <td>101</td>\n",
              "      <td>9.208</td>\n",
              "      <td>64.356</td>\n",
              "      <td>199.742</td>\n",
              "      <td>10.587</td>\n",
              "      <td>5.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Collective metered - Landlord</td>\n",
              "      <td>101</td>\n",
              "      <td>5.554</td>\n",
              "      <td>99.010</td>\n",
              "      <td>164.378</td>\n",
              "      <td>5.181</td>\n",
              "      <td>3.152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Collective unmetered - Local electrician</td>\n",
              "      <td>55</td>\n",
              "      <td>7.182</td>\n",
              "      <td>87.273</td>\n",
              "      <td>123.047</td>\n",
              "      <td>6.304</td>\n",
              "      <td>5.124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Other</td>\n",
              "      <td>97</td>\n",
              "      <td>6.443</td>\n",
              "      <td>88.660</td>\n",
              "      <td>140.650</td>\n",
              "      <td>5.961</td>\n",
              "      <td>4.238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Unelectrified</td>\n",
              "      <td>24</td>\n",
              "      <td>7.042</td>\n",
              "      <td>79.167</td>\n",
              "      <td>141.994</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Sum or average across sample</td>\n",
              "      <td>500</td>\n",
              "      <td>8.180</td>\n",
              "      <td>75.800</td>\n",
              "      <td>182.551</td>\n",
              "      <td>7.440</td>\n",
              "      <td>5.472</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('final_results')\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_results",
              "summary": "{\n  \"name\": \"final_results\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Service arrangement\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Individual metered - Utility\",\n          \"Collective metered - Utility\",\n          \"Unelectrified\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 160,\n        \"min\": 24,\n        \"max\": 500,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          122,\n          101,\n          500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure in community\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.001607722854425,\n        \"min\": 5.554,\n        \"max\": 11.557,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11.557,\n          9.208,\n          7.042\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Renters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.416329865048866,\n        \"min\": 50.0,\n        \"max\": 99.01,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          50.0,\n          64.356,\n          79.167\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average monthly income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.21746595116976,\n        \"min\": 123.047,\n        \"max\": 252.701,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          252.701,\n          199.742,\n          141.994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average monthly electricity bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.2897560048472547,\n        \"min\": 0.0,\n        \"max\": 10.587,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          8.352,\n          10.587,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Electricity burden\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9189980644671247,\n        \"min\": 0.0,\n        \"max\": 5.472,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3.305,\n          5.3,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Extended Data Figure 2"
      ],
      "metadata": {
        "id": "Lb--XdbqWZNm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the categories for 'sa' with axis labels\n",
        "sa_categories = [\n",
        "    'Individual metered - Utility',\n",
        "    'Collective metered - Utility',\n",
        "    'Collective metered - Landlord',\n",
        "    'Collective unmetered - Local electrician'\n",
        "]\n",
        "\n",
        "# Define the labels for the x-axis (formatted with line breaks)\n",
        "label = [\n",
        "    'Individual metered\\nUtility',\n",
        "    'Collective metered\\nUtility',\n",
        "    'Collective metered\\nLandlord',\n",
        "    'Collective unmetered\\nLocal electrician'\n",
        "]\n",
        "\n",
        "# Define who shuts off power\n",
        "dataframe_label = [\n",
        "    'electricity_on_off_decider_neighbor',\n",
        "    'electricity_on_off_decider_kamyufu',\n",
        "    'electricity_on_off_decider_landlord',\n",
        "    'electricity_on_off_decider_myself'\n",
        "]\n",
        "\n",
        "# Labels for the legend\n",
        "data_label_2 = ['Neighbor', 'Local electrician', 'Landlord', 'Themself']\n",
        "\n",
        "# Initialize arrays\n",
        "power_on_off_agent = np.zeros((len(dataframe_label), len(sa_categories)))\n",
        "service_arr_count = np.zeros(len(sa_categories))\n",
        "\n",
        "# Populate the matrix with counts using 'sa'\n",
        "for j in range(len(dataframe_label)):\n",
        "    for k, sa_value in enumerate(sa_categories):\n",
        "        power_on_off_agent[j, k] = df[(df['sa'] == sa_value) & (df[dataframe_label[j]] == 1)].shape[0]\n",
        "\n",
        "# Count the total number of respondents with this service arrangement\n",
        "for k, sa_value in enumerate(sa_categories):\n",
        "    service_arr_count[k] = df[df['sa'] == sa_value].shape[0]\n",
        "\n",
        "# Calculate as a percentage, avoiding division by zero\n",
        "power_on_off_agent_per = np.divide(\n",
        "    power_on_off_agent,\n",
        "    service_arr_count,\n",
        "    out=np.zeros_like(power_on_off_agent),\n",
        "    where=service_arr_count != 0\n",
        ")\n",
        "\n",
        "# Plotting\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "colors = ['#045275', '#089099', '#7CCBA2', '#F0746E', '#DC3977', '#7C1D6F']\n",
        "\n",
        "# Reverse the order of labels and the corresponding data\n",
        "label_reversed = label[::-1]\n",
        "power_on_off_agent_per_rev = power_on_off_agent_per[:, ::-1]\n",
        "\n",
        "# Calculate cumulative sums for stacking\n",
        "cumulative_sums = np.zeros(len(label_reversed))\n",
        "\n",
        "for i, data_label_item in enumerate(data_label_2):\n",
        "    values = power_on_off_agent_per_rev[i, :]\n",
        "    ax.barh(\n",
        "        label_reversed, values, left=cumulative_sums, color=colors[i % len(colors)],\n",
        "        label=data_label_item, edgecolor='black', linewidth=0.5  # Thin black border\n",
        "    )\n",
        "    cumulative_sums += values\n",
        "\n",
        "# Formatting\n",
        "ax.set_xlabel('Percentage of respondents')\n",
        "\n",
        "# Set x-axis ticks at 10% intervals\n",
        "ax.set_xticks(np.arange(0, 0.5, 0.1))\n",
        "ax.set_xticklabels([f'{int(x*100)}%' for x in np.arange(0, 0.5, 0.1)])\n",
        "\n",
        "ax.set_xlim(0, 0.4)\n",
        "plt.legend(bbox_to_anchor=(0.85, 1), frameon=False)\n",
        "ax.xaxis.set_major_formatter(mtick.PercentFormatter(1, decimals=0))\n",
        "plt.tight_layout()\n",
        "\n",
        "# Save and show plot\n",
        "fig.savefig(fig_path + \"Extended_Data_Figure_2a.svg\", format=\"svg\")\n",
        "plt.show()\n",
        "\n",
        "# Count total number of respondents included in the analysis\n",
        "total_data_points = df[df['sa'].isin(sa_categories)].shape[0]\n",
        "\n",
        "# Print the total count\n",
        "print(f\"\\n(n = {int(total_data_points)})\")"
      ],
      "metadata": {
        "id": "BwtyHjq0zlf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 820
        },
        "outputId": "0768a489-7446-4c8a-f038-bbc2853650a4"
      },
      "execution_count": 532,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "(n = 379)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the categories for 'sa' with axis labels\n",
        "sa_categories = [\n",
        "    'Individual metered - Utility',\n",
        "    'Collective metered - Utility',\n",
        "    'Collective metered - Landlord',\n",
        "    'Collective unmetered - Local electrician'\n",
        "]\n",
        "\n",
        "# Define the labels for the x-axis (formatted with line breaks)\n",
        "label = [\n",
        "    'Individual metered\\nUtility',\n",
        "    'Collective metered\\nUtility',\n",
        "    'Collective metered\\nLandlord',\n",
        "    'Collective unmetered\\nLocal electrician'\n",
        "]\n",
        "\n",
        "# Define reasons why power is shut off\n",
        "dataframe_label_legend = [\n",
        "    'power_on_off_why_nonpayment',\n",
        "    'power_on_off_why_evening',\n",
        "    'power_on_off_why_daytime',\n",
        "    'power_on_off_why_weekend',\n",
        "    'power_on_off_why_bill_low',\n",
        "    'power_on_off_why_other',\n",
        "    'power_on_off_why_dont_know',\n",
        "    'power_on_off_illegal_connection'\n",
        "]\n",
        "\n",
        "# Labels for the legend\n",
        "label_legend = ['Rent/bill nonpayment', 'Time of use agreement', 'To keep bill low', 'Connection confiscated', 'Other']\n",
        "\n",
        "# Initialize arrays\n",
        "power_on_off_reason = np.zeros((len(dataframe_label_legend), len(sa_categories)))\n",
        "\n",
        "# Populate the matrix with counts using 'sa'\n",
        "for j in range(len(dataframe_label_legend)):\n",
        "    for k, sa_value in enumerate(sa_categories):\n",
        "        power_on_off_reason[j, k] = df[(df['sa'] == sa_value) & (df[dataframe_label_legend[j]] == 1)].shape[0]\n",
        "\n",
        "# Combine categories related to time-of-use\n",
        "power_on_off_reason_combo_tou = power_on_off_reason[1:3, :].sum(axis=0)\n",
        "power_on_off_reason[1, :] = power_on_off_reason_combo_tou\n",
        "power_on_off_reason = np.delete(power_on_off_reason, [2, 3], 0)\n",
        "\n",
        "# Delete 'don't know' category\n",
        "power_on_off_reason = np.delete(power_on_off_reason, 4, 0)\n",
        "\n",
        "# Switch the order of 'other' and 'connection confiscated' categories\n",
        "power_on_off_reason = np.insert(power_on_off_reason, 3, power_on_off_reason[-1, :], axis=0)\n",
        "power_on_off_reason = np.delete(power_on_off_reason, 5, 0)\n",
        "\n",
        "# Calculate the percentage of respondents in each 'sa' category reporting a power shut-off\n",
        "total_per = power_on_off_agent_per.sum(axis=0) / power_on_off_reason.sum(axis=0)\n",
        "\n",
        "# Apply transformation\n",
        "power_on_off_reason_final = np.zeros((len(label_legend), len(sa_categories)))\n",
        "\n",
        "for k in range(len(sa_categories)):\n",
        "    power_on_off_reason_final[:, k] = power_on_off_reason[:, k] * total_per[k]\n",
        "\n",
        "# Plotting\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "colors = ['#089099', '#7CCBA2', '#FCDE9C', '#F0746E', '#DC3977', '#7C1D6F']\n",
        "\n",
        "# Reverse the order of labels and the corresponding data\n",
        "label_reversed = label[::-1]\n",
        "power_on_off_reason_final_rev = power_on_off_reason_final[:, ::-1]\n",
        "\n",
        "# Calculate cumulative sums for stacking\n",
        "cumulative_sums = np.zeros(len(label_reversed))\n",
        "\n",
        "for i, data_label_item in enumerate(label_legend):\n",
        "    values = power_on_off_reason_final_rev[i, :]\n",
        "    ax.barh(\n",
        "        label_reversed, values, left=cumulative_sums, color=colors[i % len(colors)],\n",
        "        label=data_label_item, edgecolor='black', linewidth=0.5  # Thin black border\n",
        "    )\n",
        "    cumulative_sums += values\n",
        "\n",
        "print(cumulative_sums)\n",
        "\n",
        "# Formatting\n",
        "ax.set_xlabel('Percentage of respondents')\n",
        "\n",
        "# Set x-axis ticks at 10% intervals\n",
        "ax.set_xticks(np.arange(0, 0.5, 0.1))\n",
        "ax.set_xticklabels([f'{int(x*100)}%' for x in np.arange(0, 0.5, 0.1)])\n",
        "\n",
        "ax.set_xlim(0, 0.4)\n",
        "plt.legend(bbox_to_anchor=(0.95, 1), frameon=False)\n",
        "ax.xaxis.set_major_formatter(mtick.PercentFormatter(1, decimals=0))\n",
        "plt.tight_layout()\n",
        "\n",
        "# Save and show plot\n",
        "fig.savefig(fig_path + \"Extended_Data_Figure_2b.svg\", format=\"svg\")\n",
        "plt.show()\n",
        "\n",
        "# Print total number of respondents included in the analysis for the four 'sa' categories in the graphic\n",
        "total_data_points = df[df['sa'].isin(sa_categories)].shape[0]\n",
        "\n",
        "# Print the total count\n",
        "print(f\"\\n(n = {int(total_data_points)})\")"
      ],
      "metadata": {
        "id": "6bVvFSGUWufp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "outputId": "040e1299-3c7c-498e-daa3-222c02971110"
      },
      "execution_count": 533,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.30909091 0.18811881 0.14851485 0.14754098]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "(n = 379)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Extended Data Figure 3"
      ],
      "metadata": {
        "id": "TG9FnTqga-x9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the categories for 'sa' with axis labels\n",
        "sa_categories = [\n",
        "    'Individual metered - Utility',\n",
        "    'Collective metered - Utility',\n",
        "    'Collective metered - Landlord',\n",
        "    'Collective unmetered - Local electrician'\n",
        "]\n",
        "\n",
        "# Define the labels for the x-axis (formatted with line breaks)\n",
        "label = [\n",
        "    'Individual metered\\nUtility',\n",
        "    'Collective metered\\nUtility',\n",
        "    'Collective metered\\nLandlord',\n",
        "    'Collective unmetered\\nLocal electrician'\n",
        "]\n",
        "\n",
        "# Define reasons for appliance non-use\n",
        "dataframe_label_legend = [\n",
        "    'appliance_nonuse_reason_affordability',\n",
        "    'appliance_nonuse_reason_landlord',\n",
        "    'appliance_nonuse_reason_kamyufu',\n",
        "    'appliance_nonuse_reason_limited_circuit',\n",
        "    'appliance_nonuse_reason_dont_want_need',\n",
        "    'appliance_nonuse_reason_other'\n",
        "]\n",
        "\n",
        "# Labels for the legend\n",
        "data_label = [\n",
        "    'Appliance is broken',\n",
        "    'Electricity is too expensive',\n",
        "    'Supplier limits usage',\n",
        "    'Circuit is limited',\n",
        "    'Do not need or want to use',\n",
        "    'Other'\n",
        "]\n",
        "\n",
        "# Initialize arrays\n",
        "appliance_nonuse_connection_type = np.zeros((len(dataframe_label_legend), len(sa_categories)))\n",
        "connection_count = np.zeros(len(sa_categories))\n",
        "broken_count = np.zeros(len(sa_categories))\n",
        "autonomy_count = np.zeros(len(sa_categories))\n",
        "\n",
        "# Populate the matrix with counts using 'sa'\n",
        "for j in range(len(dataframe_label_legend)):\n",
        "    for k, sa_value in enumerate(sa_categories):\n",
        "        appliance_nonuse_connection_type[j, k] = df[(df['sa'] == sa_value) & (df[dataframe_label_legend[j]] == 1)].shape[0]\n",
        "\n",
        "# Count the total number of respondents with this service arrangement\n",
        "for k, sa_value in enumerate(sa_categories):\n",
        "    connection_count[k] = df[df['sa'] == sa_value].shape[0]\n",
        "\n",
        "# Add appliances that are not used because they are broken (excluding already unused appliances)\n",
        "for k, sa_value in enumerate(sa_categories):\n",
        "    broken_count[k] = df[(df['sa'] == sa_value) & (df['broken_appliances'] == 1) & (df['appliance_use'] == 0)].shape[0]\n",
        "\n",
        "# Count respondents who pay a flat rate and don't use appliances\n",
        "for k, sa_value in enumerate(sa_categories):\n",
        "    autonomy_count[k] = df[(df['sa'] == sa_value) & (df['broken_appliances'] == 0) & (df['appliance_use'] == 0) & (df['appliance_based_billling'] == 1)].shape[0]\n",
        "\n",
        "# Combine appliance_nonuse_reason_kamyufu and appliance_nonuse_reason_landlord\n",
        "insert_row = appliance_nonuse_connection_type[1, :] + appliance_nonuse_connection_type[2, :] + autonomy_count\n",
        "appliance_nonuse_connection_type[1, :] = insert_row\n",
        "appliance_nonuse_connection_type = np.delete(appliance_nonuse_connection_type, 2, axis=0)\n",
        "\n",
        "# Insert broken appliances as a row\n",
        "appliance_nonuse_connection_type = np.insert(appliance_nonuse_connection_type, 0, broken_count, axis=0)\n",
        "\n",
        "# Calculate as a percentage, avoiding division by zero\n",
        "nonuse_reason_percent = np.divide(\n",
        "    appliance_nonuse_connection_type,\n",
        "    connection_count,\n",
        "    out=np.zeros_like(appliance_nonuse_connection_type),\n",
        "    where=connection_count != 0\n",
        ")\n",
        "\n",
        "# Plotting\n",
        "fig, ax = plt.subplots(figsize=(15, 8))\n",
        "colors = ['#089099', '#7CCBA2', '#FCDE9C', '#F0746E', '#DC3977', '#7C1D6F']\n",
        "\n",
        "# Reverse the order of labels and the corresponding data\n",
        "label_reversed = label[::-1]\n",
        "nonuse_reason_percent_reversed = nonuse_reason_percent[:, ::-1]\n",
        "\n",
        "# Calculate cumulative sums for stacking\n",
        "cumulative_sums = np.zeros(len(label_reversed))\n",
        "\n",
        "for i, data_label_item in enumerate(data_label):\n",
        "    values = nonuse_reason_percent_reversed[i, :]\n",
        "    ax.barh(\n",
        "        label_reversed, values, left=cumulative_sums, color=colors[i % len(colors)],\n",
        "        label=data_label_item, edgecolor='black', linewidth=0.5  # Thin black border\n",
        "    )\n",
        "    cumulative_sums += values\n",
        "\n",
        "appliance_nonuse = cumulative_sums\n",
        "# Formatting\n",
        "ax.set_xlabel('Percentage of respondents')\n",
        "\n",
        "# Set x-axis ticks at 10% intervals\n",
        "ax.set_xticks(np.arange(0, 0.6, 0.1))\n",
        "ax.set_xticklabels([f'{int(x*100)}%' for x in np.arange(0, 0.6, 0.1)])\n",
        "\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), frameon=False)\n",
        "ax.xaxis.set_major_formatter(mtick.PercentFormatter(1, decimals=0))\n",
        "plt.tight_layout()\n",
        "\n",
        "# Save and show plot\n",
        "fig.savefig(fig_path + \"Extended_Data_Figure_3.svg\", format=\"svg\")\n",
        "plt.show()\n",
        "\n",
        "# Print total number of respondents included in the analysis for the four 'sa' categories in the graphic\n",
        "total_data_points = df[df['sa'].isin(sa_categories)].shape[0]\n",
        "\n",
        "# Print the total count\n",
        "print(f\"\\n(n = {int(total_data_points)})\")"
      ],
      "metadata": {
        "id": "0sBVGHETbDa-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 820
        },
        "outputId": "b44df37e-88df-434d-84fc-f0b76614f482"
      },
      "execution_count": 534,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "(n = 379)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Figure S9"
      ],
      "metadata": {
        "id": "2PHMDAIhSBRo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the categories for 'sa' with axis labels\n",
        "sa_categories = {\n",
        "    \"Individual metered - Utility\": \"Individual metered\\nUtility\",\n",
        "    \"Collective metered - Utility\": \"Collective metered\\nUtility\",\n",
        "    \"Collective metered - Landlord\": \"Collective metered\\nLandlord\",\n",
        "    \"Collective unmetered - Local electrician\": \"Collective unmetered\\nLocal electrician\"\n",
        "}\n",
        "\n",
        "# Define the selected appliances\n",
        "selected_appliances = [\n",
        "    'appliance_ownership_lighting',\n",
        "    'appliance_ownership_phone_charger',\n",
        "    'appliance_ownership_tv',\n",
        "    'appliance_ownership_iron',\n",
        "    'appliance_ownership_electric_kettle',\n",
        "    'appliance_ownership_refrigerator'\n",
        "]\n",
        "\n",
        "# Filter the dataframe to include only the relevant service arrangements\n",
        "df_filtered = df[df['sa'].isin(sa_categories.keys())]\n",
        "\n",
        "# Count the number of respondents in each category\n",
        "respondent_counts = df_filtered['sa'].value_counts().reindex(sa_categories.keys(), fill_value=0)\n",
        "\n",
        "# Calculate the percentage of respondents owning each appliance per category\n",
        "df_percentage = df_filtered.groupby('sa')[selected_appliances].apply(lambda x: x.sum() / len(x) * 100)\n",
        "\n",
        "# Reorder the index to match the desired order\n",
        "df_percentage = df_percentage.reindex(index=sa_categories.keys())\n",
        "\n",
        "# Convert index labels to formatted versions\n",
        "df_percentage.index = [sa_categories[cat] for cat in df_percentage.index]\n",
        "\n",
        "# Plot the vertical clustered bar chart\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "df_percentage.plot(kind='bar', width=0.8, colormap='viridis', ax=ax)\n",
        "\n",
        "# Formatting\n",
        "plt.ylabel('Percentage of respondents (%)', fontsize=14)\n",
        "plt.xticks(rotation=45, ha='right', fontsize=14)\n",
        "plt.yticks(fontsize=12)\n",
        "ax.set_xlabel(\"\")\n",
        "\n",
        "# Set custom legend with reduced font size\n",
        "legend_labels = ['Lighting', 'Phone Charger', 'TV', 'Iron', 'Electric Kettle', 'Refrigerator']\n",
        "plt.legend(title='Appliance', labels=legend_labels, bbox_to_anchor=(1.05, 1), loc='upper left', frameon=False, fontsize=12, title_fontsize=14)\n",
        "\n",
        "plt.tight_layout()  # Adjust subplot parameters to give some padding\n",
        "fig.savefig(fig_path + \"Figure S10.png\", dpi=500)\n",
        "plt.show()\n",
        "\n",
        "# Print total number of respondents included in the analysis\n",
        "total_data_points = respondent_counts.sum()\n",
        "print(f\"\\n(n = {int(total_data_points)})\")\n",
        "\n",
        "plt.tight_layout()\n",
        "fig.savefig(fig_path + \"Figure S9.png\", dpi=500)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "PrRRtL-lSF-h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "outputId": "8e588254-8503-4615-eaba-19883cd18e4b"
      },
      "execution_count": 535,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "(n = 379)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Figure S10"
      ],
      "metadata": {
        "id": "9es4Au50G22i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine the specified reasons into one category\n",
        "df['reason_no_Yaka_no_decision_power'] = (\n",
        "    df[['reason_no_Yaka_landlord_wont_allow',\n",
        "        'reason_no_Yaka_decisionmaking_power',\n",
        "        'reason_no_Yaka_someone_wont_allow']].sum(axis=1)\n",
        ")\n",
        "\n",
        "# Define the updated reasons and their corresponding labels\n",
        "updated_reasons = {\n",
        "    'reason_no_Yaka_too_expensive': 'Too expensive',\n",
        "    'reason_no_Yaka_no_decision_power': 'No decision-making power',\n",
        "    'reason_no_Yaka_connection_time': 'Connection process takes too long',\n",
        "    'reason_no_Yaka_document': 'Doesn\\'t have required documents',\n",
        "    'reason_no_Yaka_waiting': 'Applied and waiting',\n",
        "    'reason_no_Yaka_does_not_want': 'Does not want a meter',\n",
        "    'reason_no_Yaka_other': 'Other'\n",
        "}\n",
        "\n",
        "# Calculate total respondents as all non-NA rows in the DataFrame\n",
        "total_respondents = df.dropna(subset=updated_reasons.keys(), how='all').shape[0]\n",
        "\n",
        "# Calculate respondent count for each reason\n",
        "reason_counts = {label: (df[col] == 1).sum() for col, label in updated_reasons.items() if col in df.columns}\n",
        "\n",
        "# Sort in descending order\n",
        "reason_counts = dict(sorted(reason_counts.items(), key=lambda item: item[1], reverse=True))\n",
        "\n",
        "# Convert to lists for plotting\n",
        "labels = list(reason_counts.keys())\n",
        "values = list(reason_counts.values())\n",
        "\n",
        "# Plot the horizontal bar chart\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "ax.barh(labels, values, color='#1f3028')\n",
        "\n",
        "# Formatting\n",
        "ax.set_xlabel(f'Respondent count')\n",
        "plt.gca().invert_yaxis()  # Invert y-axis for better readability\n",
        "\n",
        "plt.tight_layout()  # Adjust subplot parameters to give some padding\n",
        "\n",
        "fig.savefig(fig_path + \"Figure S10.png\", dpi = 500)\n",
        "\n",
        "# Remove title\n",
        "plt.show()\n",
        "\n",
        "print(f\"(n = {df.dropna(subset=updated_reasons.keys(), how='all').shape[0]})\")"
      ],
      "metadata": {
        "id": "tc1W4-0zG6BD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "0f7b91af-38cc-4388-e5af-406e2777e3dd"
      },
      "execution_count": 536,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(n = 500)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Figure S13"
      ],
      "metadata": {
        "id": "72Fht7UBD45F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the desired combinations with corresponding labels using 'sa' without additional formatting\n",
        "sa_mapping = {\n",
        "    \"Individual metered - Utility\": \"Individual metered - Utility\",\n",
        "    \"Collective metered - Utility\": \"Collective metered - Utility\",\n",
        "    \"Collective metered - Landlord\": \"Collective metered - Landlord\",\n",
        "    \"Collective unmetered - Local electrician\": \"Collective unmetered - Local electrician\",\n",
        "    \"Unelectrified\": \"Unelectrified\"\n",
        "}\n",
        "\n",
        "# Make a copy of the original DataFrame\n",
        "df_mtf = df.copy()\n",
        "\n",
        "# Assign service arrangement labels using the existing 'sa' column\n",
        "df_mtf['service_arrangement'] = df_mtf['sa'].map(sa_mapping)\n",
        "\n",
        "# Define the tier categorization functions\n",
        "def categorize_affordability(row):\n",
        "    if pd.isna(row['elec_payment_amount']) or pd.isna(row['total_income']):\n",
        "        return None\n",
        "    ratio = row['elec_payment_amount'] / row['total_income']\n",
        "    return 'affordability' if ratio > 0.05 else None\n",
        "\n",
        "def categorize_legality(row):\n",
        "    return 'legality' if row['payment_to_kamyufu'] == 1 or row['payment_to_no_one'] == 1 else None\n",
        "\n",
        "def categorize_quality(row):\n",
        "    return 'quality' if row['appliance_nonuse_reason_limited_circuit'] == 1 or row['appliance_damage_reason_voltage'] == 1 else None\n",
        "\n",
        "def categorize_safety(row):\n",
        "    return 'safety and health' if row['deaths_yesno'] == 1 or row['injury_yesno'] == 1 else None\n",
        "\n",
        "# Apply the categorization functions\n",
        "df_mtf['affordability_reason'] = df_mtf.apply(categorize_affordability, axis=1)\n",
        "df_mtf['legality_reason'] = df_mtf.apply(categorize_legality, axis=1)\n",
        "df_mtf['quality_reason'] = df_mtf.apply(categorize_quality, axis=1)\n",
        "df_mtf['safety_reason'] = df_mtf.apply(categorize_safety, axis=1)\n",
        "\n",
        "# Combine all reasons into a single column\n",
        "df_mtf['tier_0_reason'] = df_mtf[['affordability_reason', 'legality_reason', 'quality_reason', 'safety_reason']].bfill(axis=1).iloc[:, 0]\n",
        "\n",
        "# Identify mutually exclusive categories and combinations\n",
        "df_mtf['combination_reason'] = df_mtf.apply(lambda row: 'combination' if row[['affordability_reason', 'legality_reason', 'quality_reason', 'safety_reason']].notna().sum() > 1 else None, axis=1)\n",
        "df_mtf['exclusive_reason'] = df_mtf.apply(lambda row: row['tier_0_reason'] if row['combination_reason'] is None else 'combination', axis=1)\n",
        "\n",
        "# Filter respondents categorized as Tier 0\n",
        "df_tier_0 = df_mtf[df_mtf['exclusive_reason'].notna()]\n",
        "\n",
        "# Aggregate data by service_arrangement and exclusive_reason\n",
        "tier_0_counts = df_tier_0.groupby(['service_arrangement', 'exclusive_reason']).size().unstack(fill_value=0)\n",
        "\n",
        "# Adjust the counts for \"Unelectrified\" and \"Collective unmetered kamyufu\"\n",
        "tier_0_counts.loc[\"Unelectrified\"] = 0\n",
        "tier_0_counts.loc[\"Unelectrified\", \"availability\"] = len(df_mtf[df_mtf['service_arrangement'] == \"Unelectrified\"])\n",
        "\n",
        "tier_0_counts.loc[\"Collective unmetered - Local electrician\"] = 0\n",
        "tier_0_counts.loc[\"Collective unmetered - Local electrician\", \"legality\"] = len(df_mtf[df_mtf['service_arrangement'] == \"Collective unmetered - Local electrician\"])\n",
        "\n",
        "# Calculate the extrapolated numbers for an overall population of 1 million\n",
        "extrapolated_population = 1_000_000\n",
        "survey_population = 500\n",
        "tier_0_extrapolated = (tier_0_counts / survey_population) * extrapolated_population\n",
        "\n",
        "# Calculate the total number of people in each service arrangement\n",
        "service_arrangement_counts = df_mtf['service_arrangement'].value_counts()\n",
        "service_arrangement_extrapolated = (service_arrangement_counts / survey_population) * extrapolated_population\n",
        "\n",
        "# Reorder the x-axis labels\n",
        "x_order = [\"Unelectrified\", \"Individual metered - Utility\", \"Collective metered - Utility\", \"Collective metered - Landlord\", \"Collective unmetered - Local electrician\"]\n",
        "tier_0_extrapolated = tier_0_extrapolated.reindex(x_order)\n",
        "service_arrangement_extrapolated = service_arrangement_extrapolated.reindex(x_order)\n",
        "\n",
        "# Calculate percentage of city total\n",
        "city_total = 1_600_000\n",
        "tier_0_extrapolated_percentage = tier_0_extrapolated / city_total * 100\n",
        "service_arrangement_extrapolated_percentage = service_arrangement_extrapolated / city_total * 100\n",
        "\n",
        "# Display calculations for verification\n",
        "display(tier_0_extrapolated)\n",
        "display(service_arrangement_extrapolated)\n",
        "display(tier_0_extrapolated_percentage)\n",
        "display(service_arrangement_extrapolated_percentage)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 968
        },
        "id": "k9qZl6ahCarZ",
        "outputId": "2b8f1632-9501-4750-b942-e228ec40435d"
      },
      "execution_count": 537,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "exclusive_reason                          affordability  combination  \\\n",
              "service_arrangement                                                    \n",
              "Unelectrified                                       0.0          0.0   \n",
              "Individual metered - Utility                    50000.0      28000.0   \n",
              "Collective metered - Utility                    44000.0      28000.0   \n",
              "Collective metered - Landlord                   16000.0      12000.0   \n",
              "Collective unmetered - Local electrician            0.0          0.0   \n",
              "\n",
              "exclusive_reason                          legality  quality  \\\n",
              "service_arrangement                                           \n",
              "Unelectrified                                  0.0      0.0   \n",
              "Individual metered - Utility                   0.0  12000.0   \n",
              "Collective metered - Utility                   0.0  12000.0   \n",
              "Collective metered - Landlord                  0.0  10000.0   \n",
              "Collective unmetered - Local electrician  110000.0      0.0   \n",
              "\n",
              "exclusive_reason                          safety and health  availability  \n",
              "service_arrangement                                                        \n",
              "Unelectrified                                           0.0       48000.0  \n",
              "Individual metered - Utility                        40000.0           NaN  \n",
              "Collective metered - Utility                        30000.0           NaN  \n",
              "Collective metered - Landlord                       28000.0           NaN  \n",
              "Collective unmetered - Local electrician                0.0           0.0  "
            ],
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              "      <th>exclusive_reason</th>\n",
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              "      <th>Unelectrified</th>\n",
              "      <td>0.0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"tier_0_extrapolated\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"service_arrangement\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Individual metered - Utility\",\n          \"Collective unmetered - Local electrician\",\n          \"Collective metered - Utility\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"affordability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23832.75057562597,\n        \"min\": 0.0,\n        \"max\": 50000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          50000.0,\n          16000.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"combination\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14028.542333400146,\n        \"min\": 0.0,\n        \"max\": 28000.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          28000.0,\n          12000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"legality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49193.49550499537,\n        \"min\": 0.0,\n        \"max\": 110000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          110000.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6260.990336999411,\n        \"min\": 0.0,\n        \"max\": 12000.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          12000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"safety and health\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18460.769214742922,\n        \"min\": 0.0,\n        \"max\": 40000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          40000.0,\n          28000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"availability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33941.12549695428,\n        \"min\": 0.0,\n        \"max\": 48000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          48000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "exclusive_reason                          affordability  combination  \\\n",
              "service_arrangement                                                    \n",
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              "\n",
              "exclusive_reason                          legality  quality  \\\n",
              "service_arrangement                                           \n",
              "Unelectrified                                0.000    0.000   \n",
              "Individual metered - Utility                 0.000    0.750   \n",
              "Collective metered - Utility                 0.000    0.750   \n",
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              "Collective unmetered - Local electrician     6.875    0.000   \n",
              "\n",
              "exclusive_reason                          safety and health  availability  \n",
              "service_arrangement                                                        \n",
              "Unelectrified                                         0.000           3.0  \n",
              "Individual metered - Utility                          2.500           NaN  \n",
              "Collective metered - Utility                          1.875           NaN  \n",
              "Collective metered - Landlord                         1.750           NaN  \n",
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              "type": "dataframe",
              "variable_name": "tier_0_extrapolated_percentage",
              "summary": "{\n  \"name\": \"tier_0_extrapolated_percentage\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"service_arrangement\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Individual metered - Utility\",\n          \"Collective unmetered - Local electrician\",\n          \"Collective metered - Utility\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"affordability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.489546910976623,\n        \"min\": 0.0,\n        \"max\": 3.125,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.125,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"combination\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8767838958375092,\n        \"min\": 0.0,\n        \"max\": 1.7500000000000002,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.7500000000000002,\n          0.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"legality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0745934690622114,\n        \"min\": 0.0,\n        \"max\": 6.875000000000001,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6.875000000000001,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3913118960624632,\n        \"min\": 0.0,\n        \"max\": 0.75,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"safety and health\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1537980759214328,\n        \"min\": 0.0,\n        \"max\": 2.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.5,\n          1.7500000000000002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"availability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "service_arrangement\n",
              "Unelectrified                                3.000\n",
              "Individual metered - Utility                15.250\n",
              "Collective metered - Utility                12.625\n",
              "Collective metered - Landlord               12.625\n",
              "Collective unmetered - Local electrician     6.875\n",
              "Name: count, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>service_arrangement</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unelectrified</th>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual metered - Utility</th>\n",
              "      <td>15.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective metered - Utility</th>\n",
              "      <td>12.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective metered - Landlord</th>\n",
              "      <td>12.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective unmetered - Local electrician</th>\n",
              "      <td>6.875</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the categorization functions\n",
        "df_mtf['affordability'] = df_mtf.apply(categorize_affordability, axis=1)\n",
        "df_mtf['legality'] = df_mtf.apply(categorize_legality, axis=1)\n",
        "df_mtf['quality'] = df_mtf.apply(categorize_quality, axis=1)\n",
        "df_mtf['safety_and_health'] = df_mtf.apply(categorize_safety, axis=1)\n",
        "\n",
        "# Check if any criteria are met\n",
        "df_mtf['criteria_met'] = df_mtf[['affordability', 'legality', 'quality', 'safety_and_health']].notna().any(axis=1)\n",
        "\n",
        "# Calculate the percentage of respondents meeting at least one criteria\n",
        "percentage_meeting_criteria = df_mtf['criteria_met'].mean() * 100\n",
        "\n",
        "print(f\"Percentage of respondents meeting at least one criteria: {percentage_meeting_criteria:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWXOMNsi8_NX",
        "outputId": "f5757633-d9a7-470c-8d0f-4bb552a8182b"
      },
      "execution_count": 538,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents meeting at least one criteria: 55.20%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define x-axis labels\n",
        "x_order = [\"Unelectrified\", \"Individual metered\\nUtility\", \"Collective metered\\nUtility\",\n",
        "           \"Collective metered\\nLandlord\", \"Collective unmetered\\nLocal electrician\"]\n",
        "\n",
        "# Colors for the bars\n",
        "colors = ['#089099', '#7CCBA2', '#FCDE9C', '#F0746E', '#DC3977', '#7C1D6F']\n",
        "\n",
        "# Adjust the bar width and space\n",
        "bar_width = 0.3\n",
        "space = 0.02\n",
        "\n",
        "# Create the plot\n",
        "fig, ax1 = plt.subplots(figsize=(20, 10))  # Further increase figure size for more space on the right\n",
        "ax2 = ax1.twinx()  # Create a secondary y-axis\n",
        "\n",
        "# Plotting the total number of people in each service arrangement as a stacked bar to the right of the tick mark\n",
        "ax1.bar(np.arange(len(x_order)) + bar_width / 2 + space / 2, service_arrangement_extrapolated, width=bar_width,\n",
        "        color='lightgrey', edgecolor='black', hatch='//', label='Service arrangement total')\n",
        "\n",
        "# Plotting the extrapolated number of respondents as clustered bars to the left of the tick mark\n",
        "for i, col in enumerate(['availability', 'affordability', 'legality', 'quality', 'safety and health', 'combination']):\n",
        "    if col in tier_0_extrapolated.columns:\n",
        "        ax1.bar(np.arange(len(x_order)) - bar_width / 2 - space / 2, tier_0_extrapolated[col], width=bar_width,\n",
        "                label=col.capitalize(), color=colors[i % len(colors)], edgecolor='black')\n",
        "\n",
        "# Plotting the extrapolated number of respondents as stacked bars for middle three service arrangements\n",
        "bottoms = np.zeros(len(x_order))\n",
        "for i, col in enumerate(['availability', 'affordability', 'legality', 'quality', 'safety and health', 'combination']):\n",
        "    if col in tier_0_extrapolated.columns and col != 'availability':\n",
        "        ax1.bar(np.arange(1, 4) - bar_width / 2 - space / 2, tier_0_extrapolated[col][1:4], width=bar_width,\n",
        "                bottom=bottoms[1:4], label=col.capitalize(), color=colors[i % len(colors)], edgecolor='black')\n",
        "        bottoms[1:4] += tier_0_extrapolated[col][1:4].values\n",
        "\n",
        "# Add labels\n",
        "ax1.set_ylabel('Extrapolated Kampala-wide population')\n",
        "ax1.set_xlabel('Service arrangement')\n",
        "ax1.set_xticks(np.arange(len(x_order)))\n",
        "ax1.set_xticklabels(x_order)\n",
        "\n",
        "# Configure secondary y-axis\n",
        "ax2.set_ylabel('Percentage of city total population')\n",
        "ax2.set_yticks(np.arange(0, 21, 5))\n",
        "ax2.set_ylim(0, 20)\n",
        "ax2.set_yticklabels([f'{y}%' for y in np.arange(0, 21, 5)])\n",
        "\n",
        "# Center the x-tick labels below the center of the bars and rotate them for readability\n",
        "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=45, ha='center')\n",
        "\n",
        "# Format the primary y-axis\n",
        "ax1.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{int(x):,}'))\n",
        "\n",
        "# Remove the title\n",
        "fig.suptitle('')\n",
        "\n",
        "# Create legends\n",
        "handles, labels = ax1.get_legend_handles_labels()\n",
        "legend_labels = ['Service arrangement total', 'Availability', 'Affordability', 'Legality', 'Quality', 'Safety and health', 'Combination']\n",
        "ordered_handles = [handles[labels.index(l)] for l in legend_labels if l in labels]\n",
        "\n",
        "# Add the legend for \"Service arrangement total\"\n",
        "leg1 = ax1.legend([ordered_handles[0]], ['Service arrangement total'], loc='upper left', bbox_to_anchor=(1.1, 1), frameon=False)\n",
        "\n",
        "# Add the legend for \"Tier 5 exclusion criteria\"\n",
        "leg2 = ax1.legend(ordered_handles[1:], ['Availability', 'Affordability', 'Legality', 'Quality', 'Safety and health', 'Combination'],\n",
        "                  loc='upper left', bbox_to_anchor=(1.1, 0.9), frameon=False, title='Tier 5 exclusion criteria')\n",
        "\n",
        "# Add the legends to the plot\n",
        "ax1.add_artist(leg1)\n",
        "ax1.add_artist(leg2)\n",
        "\n",
        "# Adjust layout to ensure legends are not cut off\n",
        "plt.tight_layout(rect=[0, 0, 0.85, 2])  # Adjust the right margin\n",
        "\n",
        "fig.savefig(fig_path + \"Figure S13.png\", dpi = 500)\n",
        "\n",
        "# Show plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "g1Ku2i41bjc9",
        "outputId": "a379b2e7-7527-482a-953e-ebbff17152f1"
      },
      "execution_count": 539,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Table S5"
      ],
      "metadata": {
        "id": "HhSyPNiy1gur"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy of the original DataFrame to avoid modifying it\n",
        "df_copy = df.copy()\n",
        "\n",
        "# Replace placeholders and map labels\n",
        "df_copy['number_Yaka_connections'] = df_copy['number_Yaka_connections'].replace(999, np.nan)\n",
        "modality_labels = {\n",
        "    1: 'Unelectrified',\n",
        "    2: 'Individual metered',\n",
        "    3: 'Individual unmetered',\n",
        "    4: 'Individual dual',\n",
        "    5: 'Collective metered',\n",
        "    6: 'Collective unmetered',\n",
        "    7: 'Collective dual'\n",
        "}\n",
        "df_copy['connection_type'] = df_copy['connection_type'].map(modality_labels)\n",
        "\n",
        "# Group by and calculate initial statistics\n",
        "grouped = df_copy.groupby(['payment_to', 'connection_type'])\n",
        "results = grouped.agg(\n",
        "    Average_Users_per_Connection=('number_Yaka_connections', lambda x: (x.mean() + 1)),\n",
        "    Count=('payment_to', 'size'),\n",
        "    Average_Years_in_Community=('years_in_community', 'mean'),\n",
        "    Rent_Percentage=('rent_or_own', lambda x: (x == 'rent').mean() * 100),  # Percentage of people who rent\n",
        "    Average_Total_Income=('total_income', lambda x: x.mean() * 1000 / exchange_rate),\n",
        "    Average_Elec_Payment_Amount=('elec_payment_amount', lambda x: x.mean() * 1000 / exchange_rate)\n",
        ").reset_index()\n",
        "\n",
        "# Calculate payment structure percentages including 'Other'\n",
        "payment_structures = grouped['payment_structure'].value_counts(normalize=True).unstack(fill_value=0) * 100\n",
        "\n",
        "# Map the actual names to the expected ones\n",
        "payment_structure_mapping = {\n",
        "    'flat_rate': 'Flat rate',\n",
        "    'both_flat_rate_consumption': 'Hybrid',\n",
        "    'metered_consumption': 'Metered',\n",
        "    'other': 'Other'\n",
        "}\n",
        "\n",
        "# Apply the mapping\n",
        "payment_structures.columns = payment_structures.columns.map(payment_structure_mapping)\n",
        "\n",
        "# Ensure that all expected columns are present in the correct order\n",
        "expected_columns = ['Flat rate', 'Hybrid', 'Metered', 'Other']\n",
        "for col in expected_columns:\n",
        "    if col not in payment_structures.columns:\n",
        "        payment_structures[col] = 0.0\n",
        "payment_structures = payment_structures[expected_columns]\n",
        "\n",
        "# Normalize percentages to sum to 100 excluding 'Other'\n",
        "def normalize_percentages(row):\n",
        "    total_excluding_other = 100 - row.get('Other', 0)\n",
        "    for col in ['Flat rate', 'Hybrid', 'Metered']:\n",
        "        if total_excluding_other > 0:\n",
        "            row[col] = row[col] / total_excluding_other * 100\n",
        "    return row\n",
        "\n",
        "payment_structures = payment_structures.apply(normalize_percentages, axis=1)\n",
        "payment_structures.drop(columns='Other', inplace=True)  # Optionally drop 'Other' if not needed\n",
        "payment_structures.reset_index(inplace=True)\n",
        "\n",
        "# Merge the results\n",
        "final_results = pd.merge(results, payment_structures, on=['payment_to', 'connection_type'], how='left')\n",
        "\n",
        "# Display the final sorted results\n",
        "final_results.sort_values(by='Count', ascending=False, inplace=True)\n",
        "\n",
        "# Rename columns based on user preferences\n",
        "final_results = final_results.rename(columns={\n",
        "    \"connection_type\": \"Connection type\",\n",
        "    \"payment_to\": \"Payment recipient\",\n",
        "    \"Count\": \"n\",\n",
        "    \"Average_Users_per_Connection\": \"Average users per connection\",\n",
        "    \"Average_Years_in_Community\": \"Tenure in community\",\n",
        "    \"Rent_Percentage\": \"Renters\",\n",
        "    \"Average_Total_Income\": \"Average monthly income\",\n",
        "    \"Average_Elec_Payment_Amount\": \"Average monthly electricity bill\"\n",
        "})\n",
        "\n",
        "# Calculate Electricity burden (percentage of income spent on electricity)\n",
        "final_results[\"Electricity burden\"] = (final_results[\"Average monthly electricity bill\"] / final_results[\"Average monthly income\"]) * 100\n",
        "\n",
        "# Reorder columns with Electricity burden at the last position\n",
        "final_results = final_results[[\n",
        "    \"Connection type\", \"Payment recipient\", \"n\", \"Average users per connection\",\n",
        "    \"Metered\", \"Flat rate\", \"Hybrid\", \"Tenure in community\", \"Renters\",\n",
        "    \"Average monthly income\", \"Average monthly electricity bill\", \"Electricity burden\"\n",
        "]]\n",
        "\n",
        "# Round all numerical values to three significant figures\n",
        "final_results = final_results.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n",
        "\n",
        "display(final_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 995
        },
        "id": "ZkA2kXtn1hFc",
        "outputId": "3b028045-0f2a-45f6-e06f-c48dfadfe783"
      },
      "execution_count": 540,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-540-88851108381e>:90: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
            "  final_results = final_results.applymap(lambda x: round(x, 3) if isinstance(x, (int, float)) else x)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         Connection type  Payment recipient    n  \\\n",
              "23    Individual metered              umeme  122   \n",
              "20    Collective metered              umeme  101   \n",
              "7     Collective metered           landlord  101   \n",
              "2   Collective unmetered            kamyufu   55   \n",
              "16    Collective metered           neighbor   18   \n",
              "8   Collective unmetered           landlord   14   \n",
              "0        Collective dual            kamyufu    9   \n",
              "17  Collective unmetered           neighbor    9   \n",
              "3   Individual unmetered            kamyufu    7   \n",
              "15       Collective dual           neighbor    4   \n",
              "10  Individual unmetered           landlord    4   \n",
              "21  Collective unmetered              umeme    3   \n",
              "9        Individual dual           landlord    3   \n",
              "18  Individual unmetered             no_one    2   \n",
              "11       Collective dual   landlord kamyufu    2   \n",
              "1     Collective metered            kamyufu    2   \n",
              "12  Collective unmetered   landlord kamyufu    2   \n",
              "6        Collective dual           landlord    2   \n",
              "27    Collective metered     umeme landlord    2   \n",
              "22       Individual dual              umeme    2   \n",
              "25       Collective dual      umeme kamyufu    1   \n",
              "24         Unelectrified              umeme    1   \n",
              "26  Collective unmetered      umeme kamyufu    1   \n",
              "14       Collective dual    landlord no_one    1   \n",
              "19  Collective unmetered              other    1   \n",
              "13    Collective metered  landlord neighbor    1   \n",
              "5        Collective dual   kamyufu neighbor    1   \n",
              "4        Collective dual   kamyufu landlord    1   \n",
              "28       Individual dual       umeme no_one    1   \n",
              "\n",
              "    Average users per connection  Metered  Flat rate   Hybrid  \\\n",
              "23                         1.025   62.295     13.115   24.590   \n",
              "20                         4.690   48.515     14.851   36.634   \n",
              "7                          6.500    8.911     73.267   17.822   \n",
              "2                         13.800    0.000     81.481   18.519   \n",
              "16                         6.500   23.529     29.412   47.059   \n",
              "8                            NaN    0.000    100.000    0.000   \n",
              "0                          9.222    0.000     75.000   25.000   \n",
              "17                        11.000    0.000    100.000    0.000   \n",
              "3                          2.000    0.000    100.000    0.000   \n",
              "15                         8.500    0.000    100.000    0.000   \n",
              "10                           NaN    0.000    100.000    0.000   \n",
              "21                         3.000   33.333      0.000   66.667   \n",
              "9                          5.000    0.000     33.333   66.667   \n",
              "18                           NaN    0.000      0.000    0.000   \n",
              "11                         5.500    0.000      0.000  100.000   \n",
              "1                          5.000    0.000      0.000  100.000   \n",
              "12                           NaN    0.000    100.000    0.000   \n",
              "6                          3.500    0.000     50.000   50.000   \n",
              "27                         5.000   50.000     50.000    0.000   \n",
              "22                         6.000   50.000      0.000   50.000   \n",
              "25                         5.000    0.000      0.000  100.000   \n",
              "24                         4.000    0.000    100.000    0.000   \n",
              "26                         8.000    0.000      0.000  100.000   \n",
              "14                         4.000  100.000      0.000    0.000   \n",
              "19                           NaN    0.000    100.000    0.000   \n",
              "13                         6.000    0.000    100.000    0.000   \n",
              "5                         21.000    0.000    100.000    0.000   \n",
              "4                          6.000    0.000    100.000    0.000   \n",
              "28                         7.000    0.000    100.000    0.000   \n",
              "\n",
              "    Tenure in community  Renters  Average monthly income  \\\n",
              "23               11.557   50.000                 252.701   \n",
              "20                9.208   64.356                 199.742   \n",
              "7                 5.554   99.010                 164.378   \n",
              "2                 7.182   87.273                 123.047   \n",
              "16                4.944  100.000                 187.987   \n",
              "8                 2.571  100.000                 105.679   \n",
              "0                10.556   88.889                 152.184   \n",
              "17                5.889   66.667                  81.036   \n",
              "3                12.571   42.857                 135.440   \n",
              "15                5.250  100.000                  89.881   \n",
              "10                7.250  100.000                 109.855   \n",
              "21                2.000  100.000                 137.774   \n",
              "9                 1.333  100.000                 109.189   \n",
              "18               21.000   50.000                  99.868   \n",
              "11                1.500  100.000                 115.847   \n",
              "1                 9.500  100.000                 252.999   \n",
              "12                3.500   50.000                 119.842   \n",
              "6                 1.000  100.000                 146.473   \n",
              "27                3.000  100.000                 226.368   \n",
              "22               15.500   50.000                 173.105   \n",
              "25                1.000  100.000                 149.136   \n",
              "24                2.000  100.000                 159.789   \n",
              "26                0.000  100.000                 119.842   \n",
              "14               15.000  100.000                 199.736   \n",
              "19                0.000  100.000                 266.315   \n",
              "13                1.000  100.000                 173.105   \n",
              "5                 8.000  100.000                  79.895   \n",
              "4                 9.000  100.000                  93.210   \n",
              "28                9.000  100.000                 133.158   \n",
              "\n",
              "    Average monthly electricity bill  Electricity burden  \n",
              "23                             8.352               3.305  \n",
              "20                            10.587               5.300  \n",
              "7                              5.181               3.152  \n",
              "2                              6.304               5.124  \n",
              "16                             3.536               1.881  \n",
              "8                              3.519               3.330  \n",
              "0                              3.995               2.625  \n",
              "17                             2.811               3.469  \n",
              "3                              5.707               4.213  \n",
              "15                             3.662               4.074  \n",
              "10                             3.329               3.030  \n",
              "21                             4.439               3.222  \n",
              "9                              5.770               5.285  \n",
              "18                             0.000               0.000  \n",
              "11                            67.910              58.621  \n",
              "1                              5.992               2.368  \n",
              "12                             5.992               5.000  \n",
              "6                              4.661               3.182  \n",
              "27                             5.326               2.353  \n",
              "22                            33.289              19.231  \n",
              "25                             6.658               4.464  \n",
              "24                             7.989               5.000  \n",
              "26                             2.663               2.222  \n",
              "14                             5.326               2.667  \n",
              "19                             7.989               3.000  \n",
              "13                             2.663               1.538  \n",
              "5                              3.995               5.000  \n",
              "4                              2.663               2.857  \n",
              "28                             5.326               4.000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-51c30fe5-e707-4eb0-b221-bd68b46a3f8a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Connection type</th>\n",
              "      <th>Payment recipient</th>\n",
              "      <th>n</th>\n",
              "      <th>Average users per connection</th>\n",
              "      <th>Metered</th>\n",
              "      <th>Flat rate</th>\n",
              "      <th>Hybrid</th>\n",
              "      <th>Tenure in community</th>\n",
              "      <th>Renters</th>\n",
              "      <th>Average monthly income</th>\n",
              "      <th>Average monthly electricity bill</th>\n",
              "      <th>Electricity burden</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Individual metered</td>\n",
              "      <td>umeme</td>\n",
              "      <td>122</td>\n",
              "      <td>1.025</td>\n",
              "      <td>62.295</td>\n",
              "      <td>13.115</td>\n",
              "      <td>24.590</td>\n",
              "      <td>11.557</td>\n",
              "      <td>50.000</td>\n",
              "      <td>252.701</td>\n",
              "      <td>8.352</td>\n",
              "      <td>3.305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>umeme</td>\n",
              "      <td>101</td>\n",
              "      <td>4.690</td>\n",
              "      <td>48.515</td>\n",
              "      <td>14.851</td>\n",
              "      <td>36.634</td>\n",
              "      <td>9.208</td>\n",
              "      <td>64.356</td>\n",
              "      <td>199.742</td>\n",
              "      <td>10.587</td>\n",
              "      <td>5.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>landlord</td>\n",
              "      <td>101</td>\n",
              "      <td>6.500</td>\n",
              "      <td>8.911</td>\n",
              "      <td>73.267</td>\n",
              "      <td>17.822</td>\n",
              "      <td>5.554</td>\n",
              "      <td>99.010</td>\n",
              "      <td>164.378</td>\n",
              "      <td>5.181</td>\n",
              "      <td>3.152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>kamyufu</td>\n",
              "      <td>55</td>\n",
              "      <td>13.800</td>\n",
              "      <td>0.000</td>\n",
              "      <td>81.481</td>\n",
              "      <td>18.519</td>\n",
              "      <td>7.182</td>\n",
              "      <td>87.273</td>\n",
              "      <td>123.047</td>\n",
              "      <td>6.304</td>\n",
              "      <td>5.124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>neighbor</td>\n",
              "      <td>18</td>\n",
              "      <td>6.500</td>\n",
              "      <td>23.529</td>\n",
              "      <td>29.412</td>\n",
              "      <td>47.059</td>\n",
              "      <td>4.944</td>\n",
              "      <td>100.000</td>\n",
              "      <td>187.987</td>\n",
              "      <td>3.536</td>\n",
              "      <td>1.881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>landlord</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.571</td>\n",
              "      <td>100.000</td>\n",
              "      <td>105.679</td>\n",
              "      <td>3.519</td>\n",
              "      <td>3.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>kamyufu</td>\n",
              "      <td>9</td>\n",
              "      <td>9.222</td>\n",
              "      <td>0.000</td>\n",
              "      <td>75.000</td>\n",
              "      <td>25.000</td>\n",
              "      <td>10.556</td>\n",
              "      <td>88.889</td>\n",
              "      <td>152.184</td>\n",
              "      <td>3.995</td>\n",
              "      <td>2.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>neighbor</td>\n",
              "      <td>9</td>\n",
              "      <td>11.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>5.889</td>\n",
              "      <td>66.667</td>\n",
              "      <td>81.036</td>\n",
              "      <td>2.811</td>\n",
              "      <td>3.469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Individual unmetered</td>\n",
              "      <td>kamyufu</td>\n",
              "      <td>7</td>\n",
              "      <td>2.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>12.571</td>\n",
              "      <td>42.857</td>\n",
              "      <td>135.440</td>\n",
              "      <td>5.707</td>\n",
              "      <td>4.213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>neighbor</td>\n",
              "      <td>4</td>\n",
              "      <td>8.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>5.250</td>\n",
              "      <td>100.000</td>\n",
              "      <td>89.881</td>\n",
              "      <td>3.662</td>\n",
              "      <td>4.074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Individual unmetered</td>\n",
              "      <td>landlord</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.250</td>\n",
              "      <td>100.000</td>\n",
              "      <td>109.855</td>\n",
              "      <td>3.329</td>\n",
              "      <td>3.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>umeme</td>\n",
              "      <td>3</td>\n",
              "      <td>3.000</td>\n",
              "      <td>33.333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>66.667</td>\n",
              "      <td>2.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>137.774</td>\n",
              "      <td>4.439</td>\n",
              "      <td>3.222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Individual dual</td>\n",
              "      <td>landlord</td>\n",
              "      <td>3</td>\n",
              "      <td>5.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>33.333</td>\n",
              "      <td>66.667</td>\n",
              "      <td>1.333</td>\n",
              "      <td>100.000</td>\n",
              "      <td>109.189</td>\n",
              "      <td>5.770</td>\n",
              "      <td>5.285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Individual unmetered</td>\n",
              "      <td>no_one</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>50.000</td>\n",
              "      <td>99.868</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>landlord kamyufu</td>\n",
              "      <td>2</td>\n",
              "      <td>5.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>1.500</td>\n",
              "      <td>100.000</td>\n",
              "      <td>115.847</td>\n",
              "      <td>67.910</td>\n",
              "      <td>58.621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>kamyufu</td>\n",
              "      <td>2</td>\n",
              "      <td>5.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>9.500</td>\n",
              "      <td>100.000</td>\n",
              "      <td>252.999</td>\n",
              "      <td>5.992</td>\n",
              "      <td>2.368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>landlord kamyufu</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.500</td>\n",
              "      <td>50.000</td>\n",
              "      <td>119.842</td>\n",
              "      <td>5.992</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>landlord</td>\n",
              "      <td>2</td>\n",
              "      <td>3.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>50.000</td>\n",
              "      <td>50.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>146.473</td>\n",
              "      <td>4.661</td>\n",
              "      <td>3.182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>umeme landlord</td>\n",
              "      <td>2</td>\n",
              "      <td>5.000</td>\n",
              "      <td>50.000</td>\n",
              "      <td>50.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>226.368</td>\n",
              "      <td>5.326</td>\n",
              "      <td>2.353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Individual dual</td>\n",
              "      <td>umeme</td>\n",
              "      <td>2</td>\n",
              "      <td>6.000</td>\n",
              "      <td>50.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>50.000</td>\n",
              "      <td>15.500</td>\n",
              "      <td>50.000</td>\n",
              "      <td>173.105</td>\n",
              "      <td>33.289</td>\n",
              "      <td>19.231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>umeme kamyufu</td>\n",
              "      <td>1</td>\n",
              "      <td>5.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>149.136</td>\n",
              "      <td>6.658</td>\n",
              "      <td>4.464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Unelectrified</td>\n",
              "      <td>umeme</td>\n",
              "      <td>1</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>159.789</td>\n",
              "      <td>7.989</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>umeme kamyufu</td>\n",
              "      <td>1</td>\n",
              "      <td>8.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>119.842</td>\n",
              "      <td>2.663</td>\n",
              "      <td>2.222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>landlord no_one</td>\n",
              "      <td>1</td>\n",
              "      <td>4.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>15.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>199.736</td>\n",
              "      <td>5.326</td>\n",
              "      <td>2.667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>266.315</td>\n",
              "      <td>7.989</td>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>landlord neighbor</td>\n",
              "      <td>1</td>\n",
              "      <td>6.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>173.105</td>\n",
              "      <td>2.663</td>\n",
              "      <td>1.538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>kamyufu neighbor</td>\n",
              "      <td>1</td>\n",
              "      <td>21.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>8.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>79.895</td>\n",
              "      <td>3.995</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Collective dual</td>\n",
              "      <td>kamyufu landlord</td>\n",
              "      <td>1</td>\n",
              "      <td>6.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>9.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>93.210</td>\n",
              "      <td>2.663</td>\n",
              "      <td>2.857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Individual dual</td>\n",
              "      <td>umeme no_one</td>\n",
              "      <td>1</td>\n",
              "      <td>7.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>9.000</td>\n",
              "      <td>100.000</td>\n",
              "      <td>133.158</td>\n",
              "      <td>5.326</td>\n",
              "      <td>4.000</td>\n",
              "    </tr>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_results",
              "summary": "{\n  \"name\": \"final_results\",\n  \"rows\": 29,\n  \"fields\": [\n    {\n      \"column\": \"Connection type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Individual metered\",\n          \"Collective metered\",\n          \"Individual dual\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment recipient\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"other\",\n          \"kamyufu neighbor\",\n          \"umeme\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 1,\n        \"max\": 122,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          9,\n          122,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average users per connection\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.147483532259329,\n        \"min\": 1.025,\n        \"max\": 21.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          1.025,\n          4.69,\n          11.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Metered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.41675270426095,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          48.515,\n          33.333,\n          62.295\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Flat rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.099350635278086,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          33.333,\n          14.851,\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hybrid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.40219534527644,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          100.0,\n          36.634,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure in community\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.2902865688255085,\n        \"min\": 0.0,\n        \"max\": 21.0,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          12.571,\n          3.5,\n          11.557\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Renters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.429552600296358,\n        \"min\": 42.857,\n        \"max\": 100.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          64.356,\n          88.889,\n          50.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average monthly income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.41203147623184,\n        \"min\": 79.895,\n        \"max\": 266.315,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          135.44,\n          99.868,\n          89.881\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average monthly electricity bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.817959033836216,\n        \"min\": 0.0,\n        \"max\": 67.91,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          8.352,\n          0.0,\n          5.707\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Electricity burden\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.639838679955066,\n        \"min\": 0.0,\n        \"max\": 58.621,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          4.213,\n          0.0,\n          4.074\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Table S6"
      ],
      "metadata": {
        "id": "8hwqgxAD6-Qp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define modality labels\n",
        "modality_labels = {\n",
        "    1: 'Unconnected',\n",
        "    2: 'Individual metered',\n",
        "    3: 'Individual unmetered',\n",
        "    4: 'Individual dual',\n",
        "    5: 'Collective metered',\n",
        "    6: 'Collective unmetered',\n",
        "    7: 'Collective dual'\n",
        "}\n",
        "\n",
        "# Create copies of relevant columns\n",
        "df_copy = df.copy()\n",
        "df_copy['total_income'] = df_copy['total_income'].replace(999, np.nan) / exchange_rate * 1000\n",
        "df_copy['connection_type_copy'] = df_copy['connection_type'].map(modality_labels)\n",
        "\n",
        "# Drop NaN connection types to avoid issues\n",
        "df_copy = df_copy.dropna(subset=['connection_type_copy'])\n",
        "\n",
        "# Function to calculate percentages\n",
        "def calc_percentages(df_subset, column):\n",
        "    counts = df_subset.groupby('connection_type_copy')[column].value_counts(normalize=True).unstack()\n",
        "    return counts.fillna(0) * 100\n",
        "\n",
        "# Count respondents per connection type\n",
        "n_counts = df_copy['connection_type_copy'].value_counts().reindex(modality_labels.values(), fill_value=0)\n",
        "\n",
        "# Generate separate tables\n",
        "sex_distribution = calc_percentages(df_copy, 'gender')\n",
        "income_stats = df_copy.groupby('connection_type_copy')['total_income'].agg(['mean', lambda x: np.nanpercentile(x, 25), lambda x: np.nanpercentile(x, 75)])\n",
        "income_stats.columns = [\"Average monthly income (mean, USD/mo)\", \"Average monthly income (25th percentile, USD/mo)\", \"Average monthly income (75th percentile, USD/mo)\"]\n",
        "tenancy_distribution = calc_percentages(df_copy, 'rent_or_own')\n",
        "business_distribution = calc_percentages(df_copy, 'home_business_both')\n",
        "refugee_distribution = calc_percentages(df_copy, 'refugee_status')\n",
        "refugee_distribution.columns = [\"Refugee status (refugee %)\", \"Refugee status (non-refugee %)\"]\n",
        "tenancy_stats = df_copy.groupby('connection_type_copy')['years_in_community'].agg(['mean', lambda x: np.nanpercentile(x, 25), lambda x: np.nanpercentile(x, 75)])\n",
        "tenancy_stats.columns = [\"Average tenancy in community (mean, years)\", \"Average tenancy in community (25th percentile, years)\", \"Average tenancy in community (75th percentile, years)\"]\n",
        "\n",
        "# Display separate tables\n",
        "print(\"Respondent Count (n):\")\n",
        "display(pd.DataFrame(n_counts, columns=['n']))\n",
        "\n",
        "print(\"\\nRespondent Sex Distribution:\")\n",
        "display(sex_distribution)\n",
        "\n",
        "print(\"\\nSelf-reported Income:\")\n",
        "display(income_stats)\n",
        "\n",
        "print(\"\\nTenancy Status Distribution:\")\n",
        "display(tenancy_distribution)\n",
        "\n",
        "print(\"\\nBusiness Status Distribution:\")\n",
        "display(business_distribution)\n",
        "\n",
        "print(\"\\nRefugee Status Distribution:\")\n",
        "display(refugee_distribution)\n",
        "\n",
        "print(\"\\nAverage Tenancy in Community:\")\n",
        "display(tenancy_stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SyJPZU5jHB0G",
        "outputId": "9850f41e-2be8-4128-8dfd-4fdb2bf06553"
      },
      "execution_count": 541,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Respondent Count (n):\n"
          ]
        },
        {
          "output_type": "display_data",
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              "      const quickchartButtonEl =\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sex_distribution')\"\n",
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            "\n",
            "Self-reported Income:\n"
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          "data": {
            "text/plain": [
              "                      Average monthly income (mean, USD/mo)  \\\n",
              "connection_type_copy                                          \n",
              "Collective dual                                  132.181077   \n",
              "Collective metered                               183.300900   \n",
              "Collective unmetered                             118.666693   \n",
              "Individual dual                                  134.489141   \n",
              "Individual metered                               252.700638   \n",
              "Individual unmetered                             122.885410   \n",
              "Unconnected                                      141.994386   \n",
              "\n",
              "                      Average monthly income (25th percentile, USD/mo)  \\\n",
              "connection_type_copy                                                     \n",
              "Collective dual                                              79.894539   \n",
              "Collective metered                                          119.841809   \n",
              "Collective unmetered                                         74.568237   \n",
              "Individual dual                                             133.157565   \n",
              "Individual metered                                          133.157565   \n",
              "Individual unmetered                                        106.526052   \n",
              "Unconnected                                                  79.894539   \n",
              "\n",
              "                      Average monthly income (75th percentile, USD/mo)  \n",
              "connection_type_copy                                                    \n",
              "Collective dual                                             149.136473  \n",
              "Collective metered                                          213.052105  \n",
              "Collective unmetered                                        123.570221  \n",
              "Individual dual                                             153.131200  \n",
              "Individual metered                                          266.315131  \n",
              "Individual unmetered                                        145.141746  \n",
              "Unconnected                                                 153.131200  "
            ],
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              "      <th></th>\n",
              "      <th>Average monthly income (mean, USD/mo)</th>\n",
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              "      <th>Collective dual</th>\n",
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            "Tenancy Status Distribution:\n"
          ]
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          "data": {
            "text/plain": [
              "rent_or_own                 own       rent\n",
              "connection_type_copy                      \n",
              "Collective dual        4.761905  95.238095\n",
              "Collective metered    16.444444  83.555556\n",
              "Collective unmetered  12.790698  87.209302\n",
              "Individual dual       16.666667  83.333333\n",
              "Individual metered    50.000000  50.000000\n",
              "Individual unmetered  35.714286  64.285714\n",
              "Unconnected           20.833333  79.166667"
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              "      <th>rent_or_own</th>\n",
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              "      <th>rent</th>\n",
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              "      <th>connection_type_copy</th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Collective dual</th>\n",
              "      <td>4.761905</td>\n",
              "      <td>95.238095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective metered</th>\n",
              "      <td>16.444444</td>\n",
              "      <td>83.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective unmetered</th>\n",
              "      <td>12.790698</td>\n",
              "      <td>87.209302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual dual</th>\n",
              "      <td>16.666667</td>\n",
              "      <td>83.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual metered</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
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              "    <tr>\n",
              "      <th>Individual unmetered</th>\n",
              "      <td>35.714286</td>\n",
              "      <td>64.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unconnected</th>\n",
              "      <td>20.833333</td>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tenancy_distribution",
              "summary": "{\n  \"name\": \"tenancy_distribution\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"connection_type_copy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Collective dual\",\n          \"Collective metered\",\n          \"Individual unmetered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"own\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.347319667082182,\n        \"min\": 4.761904761904762,\n        \"max\": 50.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4.761904761904762,\n          16.444444444444446,\n          35.714285714285715\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.34731966708218,\n        \"min\": 50.0,\n        \"max\": 95.23809523809523,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          95.23809523809523,\n          83.55555555555556,\n          64.28571428571429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Business Status Distribution:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "home_business_both         both   business       home\n",
              "connection_type_copy                                 \n",
              "Collective dual        4.761905  14.285714  80.952381\n",
              "Collective metered     8.888889  14.666667  76.444444\n",
              "Collective unmetered   3.488372  20.930233  75.581395\n",
              "Individual dual        0.000000  33.333333  66.666667\n",
              "Individual metered     8.196721  20.491803  71.311475\n",
              "Individual unmetered   0.000000  28.571429  71.428571\n",
              "Unconnected           16.666667   4.166667  79.166667"
            ],
            "text/html": [
              "\n",
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              "      <th>home_business_both</th>\n",
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              "      <th>home</th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Collective dual</th>\n",
              "      <td>4.761905</td>\n",
              "      <td>14.285714</td>\n",
              "      <td>80.952381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective metered</th>\n",
              "      <td>8.888889</td>\n",
              "      <td>14.666667</td>\n",
              "      <td>76.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective unmetered</th>\n",
              "      <td>3.488372</td>\n",
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              "      <td>75.581395</td>\n",
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              "      <th>Unconnected</th>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-80378ba9-4981-467f-941b-18d5198ef4b2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-80378ba9-4981-467f-941b-18d5198ef4b2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-acbbc9c0-c626-4bf4-9d28-291bbab78650\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-acbbc9c0-c626-4bf4-9d28-291bbab78650')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-acbbc9c0-c626-4bf4-9d28-291bbab78650 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_57723254-8427-465e-be90-cb497ec7c2d9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('business_distribution')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_57723254-8427-465e-be90-cb497ec7c2d9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('business_distribution');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "business_distribution",
              "summary": "{\n  \"name\": \"business_distribution\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"connection_type_copy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Collective dual\",\n          \"Collective metered\",\n          \"Individual unmetered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"both\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.870692675492246,\n        \"min\": 0.0,\n        \"max\": 16.666666666666664,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.761904761904762,\n          8.88888888888889,\n          16.666666666666664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"business\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.681096228590182,\n        \"min\": 4.166666666666666,\n        \"max\": 33.33333333333333,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          14.285714285714285,\n          14.666666666666666,\n          28.57142857142857\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"home\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.988693400978245,\n        \"min\": 66.66666666666666,\n        \"max\": 80.95238095238095,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          80.95238095238095,\n          76.44444444444444,\n          71.42857142857143\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Refugee Status Distribution:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                      Refugee status (refugee %)  \\\n",
              "connection_type_copy                               \n",
              "Collective dual                       100.000000   \n",
              "Collective metered                     97.333333   \n",
              "Collective unmetered                   96.511628   \n",
              "Individual dual                        66.666667   \n",
              "Individual metered                     98.360656   \n",
              "Individual unmetered                   85.714286   \n",
              "Unconnected                           100.000000   \n",
              "\n",
              "                      Refugee status (non-refugee %)  \n",
              "connection_type_copy                                  \n",
              "Collective dual                             0.000000  \n",
              "Collective metered                          2.666667  \n",
              "Collective unmetered                        3.488372  \n",
              "Individual dual                            33.333333  \n",
              "Individual metered                          1.639344  \n",
              "Individual unmetered                       14.285714  \n",
              "Unconnected                                 0.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b2ad2a8-c039-4977-91ab-9a9b9b167184\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Refugee status (refugee %)</th>\n",
              "      <th>Refugee status (non-refugee %)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>connection_type_copy</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Collective dual</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective metered</th>\n",
              "      <td>97.333333</td>\n",
              "      <td>2.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective unmetered</th>\n",
              "      <td>96.511628</td>\n",
              "      <td>3.488372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual dual</th>\n",
              "      <td>66.666667</td>\n",
              "      <td>33.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual metered</th>\n",
              "      <td>98.360656</td>\n",
              "      <td>1.639344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual unmetered</th>\n",
              "      <td>85.714286</td>\n",
              "      <td>14.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unconnected</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b2ad2a8-c039-4977-91ab-9a9b9b167184')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9b2ad2a8-c039-4977-91ab-9a9b9b167184 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9b2ad2a8-c039-4977-91ab-9a9b9b167184');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-90d6296d-882c-4099-a3cb-647f925b5ff0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ba14dda8-1347-4ec0-a87e-73a50405fdc4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('refugee_distribution')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ba14dda8-1347-4ec0-a87e-73a50405fdc4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('refugee_distribution');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "refugee_distribution",
              "summary": "{\n  \"name\": \"refugee_distribution\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"connection_type_copy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Collective dual\",\n          \"Collective metered\",\n          \"Individual unmetered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Refugee status (refugee %)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.237211025827252,\n        \"min\": 66.66666666666666,\n        \"max\": 100.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          100.0,\n          97.33333333333334,\n          85.71428571428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Refugee status (non-refugee %)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.237211025827246,\n        \"min\": 0.0,\n        \"max\": 33.33333333333333,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0,\n          2.666666666666667,\n          14.285714285714285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Average Tenancy in Community:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                      Average tenancy in community (mean, years)  \\\n",
              "connection_type_copy                                               \n",
              "Collective dual                                         7.333333   \n",
              "Collective metered                                      7.137778   \n",
              "Collective unmetered                                    5.779070   \n",
              "Individual dual                                         7.333333   \n",
              "Individual metered                                     11.557377   \n",
              "Individual unmetered                                   12.785714   \n",
              "Unconnected                                             7.041667   \n",
              "\n",
              "                      Average tenancy in community (25th percentile, years)  \\\n",
              "connection_type_copy                                                          \n",
              "Collective dual                                                    2.00       \n",
              "Collective metered                                                 2.00       \n",
              "Collective unmetered                                               2.00       \n",
              "Individual dual                                                    2.00       \n",
              "Individual metered                                                 2.25       \n",
              "Individual unmetered                                               4.25       \n",
              "Unconnected                                                        2.00       \n",
              "\n",
              "                      Average tenancy in community (75th percentile, years)  \n",
              "connection_type_copy                                                         \n",
              "Collective dual                                                    8.00      \n",
              "Collective metered                                                 9.00      \n",
              "Collective unmetered                                               6.00      \n",
              "Individual dual                                                    8.00      \n",
              "Individual metered                                                20.00      \n",
              "Individual unmetered                                              12.00      \n",
              "Unconnected                                                        9.25      "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-57cb74aa-a883-4cff-bc83-46009f8552c9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Average tenancy in community (mean, years)</th>\n",
              "      <th>Average tenancy in community (25th percentile, years)</th>\n",
              "      <th>Average tenancy in community (75th percentile, years)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>connection_type_copy</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Collective dual</th>\n",
              "      <td>7.333333</td>\n",
              "      <td>2.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective metered</th>\n",
              "      <td>7.137778</td>\n",
              "      <td>2.00</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Collective unmetered</th>\n",
              "      <td>5.779070</td>\n",
              "      <td>2.00</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual dual</th>\n",
              "      <td>7.333333</td>\n",
              "      <td>2.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual metered</th>\n",
              "      <td>11.557377</td>\n",
              "      <td>2.25</td>\n",
              "      <td>20.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Individual unmetered</th>\n",
              "      <td>12.785714</td>\n",
              "      <td>4.25</td>\n",
              "      <td>12.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unconnected</th>\n",
              "      <td>7.041667</td>\n",
              "      <td>2.00</td>\n",
              "      <td>9.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57cb74aa-a883-4cff-bc83-46009f8552c9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-57cb74aa-a883-4cff-bc83-46009f8552c9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-57cb74aa-a883-4cff-bc83-46009f8552c9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
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              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
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              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('tenancy_stats')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_4275a695-7785-4457-8c59-978e758d5612 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('tenancy_stats');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tenancy_stats",
              "summary": "{\n  \"name\": \"tenancy_stats\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"connection_type_copy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Collective dual\",\n          \"Collective metered\",\n          \"Individual unmetered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average tenancy in community (mean, years)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6388943319752527,\n        \"min\": 5.77906976744186,\n        \"max\": 12.785714285714286,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          7.333333333333333,\n          7.137777777777778,\n          7.041666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average tenancy in community (25th percentile, years)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8398554297360605,\n        \"min\": 2.0,\n        \"max\": 4.25,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.0,\n          2.25,\n          4.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average tenancy in community (75th percentile, years)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.63391996971401,\n        \"min\": 6.0,\n        \"max\": 20.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8.0,\n          9.0,\n          9.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts = df['connection_type'].map({\n",
        "    1: 'Unconnected',\n",
        "    2: 'Individual metered',\n",
        "    3: 'Individual unmetered',\n",
        "    4: 'Individual dual',\n",
        "    5: 'Collective metered',\n",
        "    6: 'Collective unmetered',\n",
        "    7: 'Collective dual'\n",
        "}).value_counts()\n",
        "\n",
        "# Add total sum row\n",
        "counts.loc['Total'] = counts.sum()\n",
        "\n",
        "# Display the result\n",
        "print(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qq6rZJ7KT93",
        "outputId": "0db9c4b3-b385-459d-9f3c-c441367882cf"
      },
      "execution_count": 544,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "connection_type\n",
            "Collective metered      225\n",
            "Individual metered      122\n",
            "Collective unmetered     86\n",
            "Unconnected              24\n",
            "Collective dual          21\n",
            "Individual unmetered     14\n",
            "Individual dual           6\n",
            "Total                   498\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# In-text statistics"
      ],
      "metadata": {
        "id": "YJL7-qpL98o6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Number of unique service arrangements"
      ],
      "metadata": {
        "id": "vc2vXv8t-I_y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of unique service arrangements: {df_copy.groupby(['payment_to', 'connection_type']).ngroups}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wm5J4sZ-ZUk",
        "outputId": "8541a7f4-f5c6-4924-9825-84d997a149f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique service arrangements: 29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents with dual service arrangement types"
      ],
      "metadata": {
        "id": "kqFi7UMx-0na"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents with dual service arrangements: {df[df['connection_type'].isin([4, 7])].shape[0] / len(df) * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhZp2Jmg-3UF",
        "outputId": "9dea3e6b-5968-44ba-c6c3-e73f72256781"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents with dual service arrangements: 5.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents that are unelectrified"
      ],
      "metadata": {
        "id": "_fU7LBbF_MpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents who are unelectrified: {df[df['sa'] == 'Unelectrified'].shape[0] / len(df) * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vs0bbv80_MRw",
        "outputId": "07cc0bbf-13ae-4283-ce1e-3fcb35ba7512"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents who are unelectrified: 4.80%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents with metered connection types"
      ],
      "metadata": {
        "id": "e-9XRdc6_iDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents with metered connection types: {df[df['connection_type'].isin([2, 5])].shape[0] / len(df) * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QeKcV94_zvW",
        "outputId": "792e1f7a-d43c-425a-9345-dad18d20e872"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents with metered connection types: 69.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of metered respondents with a collective metered connection type\n",
        "\n"
      ],
      "metadata": {
        "id": "RpfnFYeT_tGC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of metered respondents with a collective metered connection type: {df[df['connection_type'] == 5].shape[0] / df[df['connection_type'].isin([2, 5])].shape[0] * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcrQEh6XAKar",
        "outputId": "1eb3cfba-9372-4ebb-96a4-a53e5dcbe456"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of metered respondents with a collective metered connection type: 64.84%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Connection cost compared to average monthly income"
      ],
      "metadata": {
        "id": "2Plb7dsoAJ99"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "average_monthly_income = df[\"total_income\"].replace(999, np.nan).mean() / exchange_rate * 1000\n",
        "print(f\"171–682 USD as a fraction of average monthly income: {171 / average_monthly_income:.0f} - {682 / average_monthly_income:.0f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z6HuHqKxA1Rs",
        "outputId": "e61ff15d-be03-4d0a-859b-d096bb03fc5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "171–682 USD as a fraction of average monthly income: 1 - 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents with unmetered connection types"
      ],
      "metadata": {
        "id": "uqitJQ9BBMaZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents with un connection types: {df[df['connection_type'].isin([3, 6, 4, 7])].shape[0] / len(df) * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tpsEiaHBRu0",
        "outputId": "d4277d68-81c8-41c2-bc1a-07d77e3cf75c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents with un connection types: 25.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Count and percentage of respondents not making payments to anyone"
      ],
      "metadata": {
        "id": "x4AwVPryBjSq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Count and percentage of respondents not making regular payments: {df[df['payment recipient'] == 'No one/other'].shape[0]} ({df[df['payment recipient'] == 'No one/other'].shape[0] / len(df) * 100:.2f}%)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NLKWLv2LBRsx",
        "outputId": "f8e9d090-5ebe-41ec-a0e5-c89aece71519"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count and percentage of respondents not making regular payments: 5 (1.00%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents paying directly to utility"
      ],
      "metadata": {
        "id": "yKYBYUq6CEhe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents paying utility: {df[df['payment recipient'] == 'Umeme'].shape[0] / len(df) * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGU5RhgdCHTW",
        "outputId": "c4a8e553-9f93-4095-9bcd-7cef96db15e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents paying utility: 45.80%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents paying to landlord, neighbor, or kamyufu"
      ],
      "metadata": {
        "id": "H2nVSP92Ce3M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents paying landlord, neighbor, or kamyufu: {df[df['payment recipient'].isin(['Landlord', 'Neighbor', 'Kamyufu'])].shape[0] / len(df) * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F10tkG-cCdE0",
        "outputId": "92c463e0-6fe6-4b4d-ffdc-8e220621d735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents paying landlord, neighbor, or kamyufu: 45.80%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents paying more than one bill collector"
      ],
      "metadata": {
        "id": "AjMzUBYPD9xk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents paying more than one bill collector: {df[df['payment recipient'].isin(['Two bill collectors'])].shape[0] / len(df) * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0it6x3sDHr3",
        "outputId": "b6cd603c-1c5e-48be-cf6f-6bb448e61034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents paying more than one bill collector: 2.20%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents using top four service arrangements"
      ],
      "metadata": {
        "id": "gNRnrt2_DHcQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents represented by the four service arrangements: {df[df.apply(lambda row: (row['connection_type'] == 2 and row['payment recipient'] == 'Umeme') or (row['connection_type'] == 5 and row['payment recipient'] == 'Umeme') or (row['connection_type'] == 5 and row['payment recipient'] == 'Landlord') or (row['connection_type'] == 6 and row['payment recipient'] == 'Kamyufu'), axis=1)].shape[0] / len(df) * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T67crnIiEBJG",
        "outputId": "12d2dc3b-e9e8-4413-b012-14cf9ecf8f7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents represented by the four service arrangements: 75.80%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents using individual metered-utility service arrangement"
      ],
      "metadata": {
        "id": "m9kHPwZfEOTk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents using individual metered - utility service arrangement': {df[(df['connection_type'] == 2) & (df['payment recipient'] == 'Umeme')].shape[0] / len(df) * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NPLGE6mtEN_f",
        "outputId": "34c4d3fd-8e75-4722-dfdd-002d84d5d56c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents using individual metered - utility service arrangement': 24.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents using individual metered-utility service arrangement and paying a flat rate structure"
      ],
      "metadata": {
        "id": "aure8I0oFNkp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of flat rate payment structures among users with an individual metered - utility service arrangement': {df[(df['sa'] == 'Individual metered - Utility') & (df['payment_structure'] == 'flat_rate')].shape[0] / df[df['sa'] == 'Individual metered - Utility'].shape[0] * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5HnqzJqFN8l",
        "outputId": "d6517de9-4495-4bdf-da77-f976d65759fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of flat rate payment structures among user individual metered - utility service arrangement': 13.11%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents using individual metered-utility service arrangement and paying a hybrid structure"
      ],
      "metadata": {
        "id": "Dv-0dLD6FeXp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of hybrid payment structures among users with an individual metered - utility service arrangement': {df[(df['sa'] == 'Individual metered - Utility') & (df['payment_structure'] == 'both_flat_rate_consumption')].shape[0] / df[df['sa'] == 'Individual metered - Utility'].shape[0] * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhakdVCnFide",
        "outputId": "91d00e92-5edb-444d-8711-b9c58dcb05d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of hybrid payment structures among users with an individual metered - utility service arrangement': 24.59%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents using collective metered-utility or collective metered-landlord service arrangements"
      ],
      "metadata": {
        "id": "5GV-u6n6FsLv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents using collective metered - utility service arrangement': {df[(df['connection_type'] == 5) & (df['payment recipient'] == 'Umeme')].shape[0] / len(df) * 100:.2f}%\")\n",
        "print(f\"Percentage of respondents using collective metered - landlord service arrangement': {df[(df['connection_type'] == 5) & (df['payment recipient'] == 'Landlord')].shape[0] / len(df) * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fojkr598Fiby",
        "outputId": "1426e73e-8a60-4fbc-b520-f7a69609e861"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents using collective metered - utility service arrangement': 20.20%\n",
            "Percentage of respondents using collective metered - landlord service arrangement': 20.20%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents using a collective unmetered-local electrician service arrangement"
      ],
      "metadata": {
        "id": "LR1mGVzQF88k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents using collective unmetered - local electrician service arrangement': {df[(df['connection_type'] == 6) & (df['payment recipient'] == 'Kamyufu')].shape[0] / len(df) * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdHRpE5jGEOd",
        "outputId": "642a3e48-f808-4ed2-9c72-df75baf47692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents using collective unmetered - local electrician service arrangement': 11.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents using a collective unmetered-local electrician service arrangement and paying a flat rate structure"
      ],
      "metadata": {
        "id": "qDTb4IiJGLdF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of flat rate payment structures among users with a collective unmetered - local electrician service arrangement': {df[(df['sa'] == 'Collective unmetered - Local electrician') & (df['payment_structure'] == 'flat_rate')].shape[0] / df[df['sa'] == 'Collective unmetered - Local electrician'].shape[0] * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8L8ZqsnaGKJI",
        "outputId": "53736451-63ef-49e4-c434-6bd4e6fff3f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of flat rate payment structures among users with a collective unmetered - local electrician service arrangement': 80.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of reasons for not having individual metered connection"
      ],
      "metadata": {
        "id": "l7N_NaXBGj3z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print({label: f\"{(count / sum(reason_counts.values())) * 100:.2f}%\" for label, count in reason_counts.items()})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCQplCf6G14Y",
        "outputId": "77caf5be-2609-4dec-e7f3-61d15aa27e12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'No decision-making power': '34.32%', 'Too expensive': '32.67%', 'Connection process takes too long': '11.55%', 'Does not want a meter': '11.22%', \"Doesn't have required documents\": '5.28%', 'Other': '3.30%', 'Applied and waiting': '1.65%'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of all respondents who are renters"
      ],
      "metadata": {
        "id": "MY02nlfrHcPB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of renters among all respondents: {((df['rent_or_own'] == 'rent').sum() / len(df)) * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsCP4nJXHegI",
        "outputId": "ee40a50f-d8bf-45b2-c7fa-22b263148ddc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of renters among all respondents: 75.80%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of unelectrified respondents who previously had a connection"
      ],
      "metadata": {
        "id": "wFGFVYvkHzEG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents with previous electricity connection: {((df['previous_electricity_connection'] == 1).sum() / df['previous_electricity_connection'].notna().sum()) * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WjQG0ziRH4ps",
        "outputId": "0f546c0f-2223-4d37-d519-463f62df580e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents with previous electricity connection: 69.23%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents reporting broken appliances by service arrangement"
      ],
      "metadata": {
        "id": "2Lw2BlRvFPwN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the relevant connection types\n",
        "connection_types = [2, 5, 6]\n",
        "\n",
        "# Initialize arrays to store the broken appliance count and total respondents for each connection type\n",
        "broken_count = np.zeros(len(connection_types))\n",
        "connection_count = np.zeros(len(connection_types))\n",
        "\n",
        "# Populate the broken_count and connection_count arrays\n",
        "for k, conn_type in enumerate(connection_types):\n",
        "    broken_count[k] = df[(df['connection_type'] == conn_type) & (df['broken_appliances'] == 1) & (df['appliance_use'] == 0)].shape[0]\n",
        "    connection_count[k] = df[df['connection_type'] == conn_type].shape[0]\n",
        "\n",
        "# Calculate the percentage of broken appliances for each connection type\n",
        "broken_percentage = (broken_count / connection_count) * 100\n",
        "\n",
        "# Create a DataFrame to display the results\n",
        "broken_percentage_df = pd.DataFrame({\n",
        "    \"Connection Type\": ['Individual metered', 'Collective metered', 'Collective unmetered'],\n",
        "    \"Broken Appliances Percentage\": broken_percentage\n",
        "})\n",
        "\n",
        "display(broken_percentage_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "a3ro3EePJOqZ",
        "outputId": "04e4ab32-7054-457e-ea6a-51b82c3131c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        Connection Type  Broken Appliances Percentage\n",
              "0    Individual metered                      9.016393\n",
              "1    Collective metered                     12.888889\n",
              "2  Collective unmetered                     24.418605"
            ],
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              "      <th>Connection Type</th>\n",
              "      <th>Broken Appliances Percentage</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Collective metered</td>\n",
              "      <td>12.888889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Collective unmetered</td>\n",
              "      <td>24.418605</td>\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "\n",
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              "      }\n",
              "\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('broken_percentage_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('broken_percentage_df');\n",
              "      }\n",
              "      })();\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "broken_percentage_df",
              "summary": "{\n  \"name\": \"broken_percentage_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Connection Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Individual metered\",\n          \"Collective metered\",\n          \"Collective unmetered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Broken Appliances Percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.012060457550312,\n        \"min\": 9.01639344262295,\n        \"max\": 24.418604651162788,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9.01639344262295,\n          12.88888888888889,\n          24.418604651162788\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents with individual metered-utility service arrangements foregoing appliance use"
      ],
      "metadata": {
        "id": "WvS2RBx0K4vK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents with individual metered-utility service arrangement who forego using appliances weekly: {appliance_nonuse[-1] * 100:.1f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWf_TXQWLBt5",
        "outputId": "9bc909ee-5e72-4ceb-fa3b-b57842c22506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents with individual metered-utility service arrangement who forego using appliances weekly: 45.9%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Percentage of respondents not meeting criteria for MTF Tier 5"
      ],
      "metadata": {
        "id": "FzVhNfRrMp3K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Percentage of respondents not meeting Tier 5 criteria: {percentage_meeting_criteria:.2f}%\")"
      ],
      "metadata": {
        "id": "ejn9TNoLL8RN",
        "outputId": "8b21df9c-2fda-4494-9ee5-db1f1fda8ebf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of respondents not meeting Tier 5 criteria: 55.20%\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}